# Deep Learning-Based Lesion Detection in Endoscopy: A Systematic Literature Review

Naim Rochmawati, Chastine Fatichah, Bilqis Amaliah, Agus Budi Raharjo, Frédéric Dumont, Emilie Thibaureau, Cédric Dumas

► To cite this version:

Naim Rochmawati, Chastine Fatichah, Bilqis Amaliah, Agus Budi Raharjo, Frédéric Dumont, et al. Deep Learning-Based Lesion Detection in Endoscopy: A Systematic Literature Review. IEEE Access, 2025, 13, pp.43532 - 43556. 10.1109/access.2025.3548167. hal-05086996

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Received 30 January 2025, accepted 28 February 2025, date of publication 5 March 2025, date of current version 14 March 2025.

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SURVEY

# Deep Learning-Based Lesion Detection in Endoscopy: A Systematic Literature Review

NAIM ROCHMAWATI<sup>1,2</sup>, CHASTINE FATICHAH<sup>1,1</sup>, (Member, IEEE), BILQIS AMALIAH<sup>1,1</sup>, (Member, IEEE), AGUS BUDI RAHARJO<sup>1,1</sup>, (Member, IEEE), FRÉDÉRIC DUMONT<sup>1,3</sup>, EMILIE THIBAUDEAU<sup>1,3</sup>, AND CÉDRIC DUMAS<sup>1,4</sup>

Corresponding author: Chastine Fatichah (chastine@if.its.ac.id)

This work was supported in part by the Outbound Researcher Mobility (ORM) Program of Institut Teknologi Sepuluh Nopember (ITS), in 2024; and in part by Indonesia Endowment Fund for Education (LPDP) through the Higher Education Endowment Fund (DAPT) Program 2023/2024.

**ABSTRACT** A lesion is an abnormal change in body tissue or organ caused by various factors such as inflammation, infection, or abnormal cell growth. The presence of lesions often indicates a serious condition requiring medical attention. Early detection of lesions is crucial for effective treatment and preventing disease progression. Endoscopy is a key tool for lesion identification and evaluation, and the integration of deep learning techniques has further enhanced its potential. This systematic literature review (SLR) examines 35 studies to provide a comprehensive overview of lesion detection using deep learning on endoscopic images. The review explores deep learning techniques, modifications to baseline models, datasets, preprocessing and augmentation methods, and evaluation metrics. A meta-analysis of average performance metrics—accuracy, precision, recall, and specificity—was also conducted to identify trends across datasets and lesion types. Additionally, emerging trends such as federated learning, lightweight models, and ethical considerations are briefly discussed, addressing critical aspects for clinical translation. While significant advancements have been made, challenges remain, including dataset biases, scalability in resource-limited settings, and ensuring generalizability. Insights gained from this review are expected to guide future developments in lesion detection models, contributing to improved diagnostic accuracy, scalability, and patient outcomes.

**INDEX TERMS** Lesion, detection, endoscopy, deep learning, systematic literature review.

## I. INTRODUCTION

Endoscopic lesions, often the first sign of more serious medical conditions such as cancer, Endoscopic lesions have been a primary objective in optimizing early diagnostic and therapeutic strategies [1]. Lesions can be defined as any abnormal changes in tissue or organs of the body, which can stem from various causes, including inflammatory processes, infections [2], or abnormal cell growth. Lesions can be found in various organs such as the gastrointestinal tract, respiratory system, urinary tract, and reproductive system.

The associate editor coordinating the review of this manuscript and approving it for publication was Mohamad Afendee Mohamed.

Endoscopy is a medical technique that utilizes a specialized instrument called an endoscope to visualize internal organs directly. The endoscopic procedure enables doctors to see the internal organ conditions in detail, aiding in the identification and evaluation of any potential lesions [3]. Endoscopic lesions can manifest as spots, polyps, altered tissues, or other structural changes that require further medical attention.

In healthcare, the detection and analysis of endoscopic lesions play a crucial role [4]. Lesions can indicate the presence of certain diseases, including pre-cancerous or established cancerous conditions [5]. Therefore, early and accurate detection of endoscopic lesions is a crucial initial step in effective treatment efforts.

<footnote n="1">¹Department of Informatics, Faculty of Intelligent Electrical and Informatics Technology, Institut Teknologi Sepuluh Nopember, Surabaya 60111, Indonesia</footnote>

<footnote n="2">²Department of Informatics, Faculty of Engineering, Universitas Negeri Surabaya, Surabaya 60231, Indonesia</footnote>

<footnote n="3">³Institut de Cancérologie de l'Ouest, 44805 Saint-Herblain, France</footnote>

<footnote n="4">⁴Institut Mines-Télécom Atlantique, 44307 Nantes, France</footnote>
Although endoscopic techniques have advanced rapidly, challenges still exist in identifying and evaluating lesions with high accuracy. In recent years, computational technology and machine learning have been introduced into medical practice [6] to support the process of lesion detection and analysis. Computational algorithms and machine learning models can help improve accuracy, efficiency, and consistency in detecting and analyzing endoscopic lesions [7].

Deep learning represents a paradigm shift in the field of machine learning, offering unique advantages that distinguish it from conventional methods [8]. One of its main strengths lies in its ability to automatically learn hierarchical data representations, enabling it to capture complex patterns and features that may be challenging for traditional machine learning algorithms to identify [9]. Through deep neural networks, deep learning models have the ability to analyze vast datasets and derive valuable insights from them, resulting in more accurate and powerful predictions. Additionally, deep learning is particularly effective at working with unstructured data [10], including images, videos, and text, which makes it ideal for applications such as image recognition, natural language processing, and medical imaging. In endoscopic lesion detection, the complex and diverse nature of medical images demands sophisticated techniques for feature extraction and classification, tasks in which deep learning has demonstrated remarkable success.

This research focuses on lesion detection using endoscopic and laparoscopic modalities by applying deep learning techniques. Endoscopy has become a crucial diagnostic tool in the medical world due to its ability to provide direct visualization of internal organs, enabling doctors to detect and evaluate lesions accurately [11]. The use of deep learning techniques in conjunction with endoscopy carries significant implications. Deep learning can process complex medical data and identify patterns that may be difficult to detect manually. By harnessing this capability, lesion detection can be performed more accurately and efficiently, increasing the likelihood of early and targeted diagnosis. This has the potential to reduce interpretation errors [12] and improve patient care effectiveness. Thus, exploration in this field presents a great opportunity for the development of more advanced diagnostic technologies and better clinical outcomes for patients overall.

As a team with diverse backgrounds, we have a deep interest in exploring various methods used in lesion detection, the types of lesions detected, deep learning methods and their variations, the datasets used for lesion detection, data preprocessing, data augmentation, and evaluation metrics. With that background, our research questions (RQs) are designed to cover key aspects relevant to our study:

**RQ1:** What types of lesions can be detected using endoscopic and laparoscopic modalities with deep learning, and in which anatomical region are these lesions commonly found?

**Motivation:** To gain insights into the various types of lesions that have been detected by researchers previously using endoscopic and laparoscopic modalities.

**RQ2:** What deep learning methods are used in endoscopic and laparoscopic lesion detection?

**Motivation:** Understanding the latest developments in deep learning methods is crucial to determine if this technology can provide significant advantages in endoscopic lesion detection.

**RQ3:** To what extent do variations and modifications in deep learning methods affect the performance of endoscopic lesion detection, and what are the implications for improved accuracy and efficiency?

**Motivation:** Understanding variations and modifications in deep learning methods will provide insights into how this technology can be optimized to enhance endoscopic lesion detection outcomes.

**RQ4:** What datasets are used in lesion detection with endoscopic and laparoscopic modalities?

**Motivation:** Providing researchers with knowledge about various public datasets related to endoscopy and laparoscopy.

**RQ5:** What are the most effective data augmentation methods and preprocessing steps in improving the performance of deep learning models for endoscopic lesion detection?

**Motivation:** Understanding data augmentation and preprocessing techniques will help choose the optimal approach to enhance the performance of endoscopic lesion detection models.

**RQ6:** What metrics are used in lesion detection?

**Motivation:** Knowing the performance evaluation results will help assess how effective endoscopic lesion detection technology using deep learning is in supporting medical practices in the field.

This paper comprehensively outlines various crucial aspects related to lesion detection analysis in endoscopy using deep learning, including variations in deep learning, datasets, preprocessing and augmentation steps, as well as the metrics employed. While several review papers have discussed deep learning applications in lesion detection, our study differs in scope and focus. For instance, Chen et al. [13] examined lesion detection using deep learning, covering various deep learning methods, but did not address the variations or enhancements applied, nor did they discuss the types of lesions detected or the preprocessing and augmentation steps involved. Unlike Chen et al., who reviewed MRI and CT modalities, our study focuses specifically on endoscopic datasets, ensuring the relevance of data to this specific medical imaging technique. Other researchers have explored deep learning in endoscopy, but their focus was limited to specific lesions, such as gastric cancer detection by Klang et al. [14] and GI lesions by other studies [15], [16]. Several systematic literature reviews (SLRs) on deep learning in endoscopy have primarily focused on Computer-Aided Diagnosis (CAD) systems, such as [17], [18]. In contrast, our paper does not focus on CAD systems but instead provides a broader analysis of lesion detection using deep learning in endoscopy. While some SLRs primarily focus on CAD systems, our paper expands beyond CAD-centric discussions to provide a broader analysis of lesion detection using deep learning methods.
deep learning in endoscopy, including real-time deployment considerations.

This study not only answers these RQs but also incorporates a meta-analysis of the average metrics across datasets and lesion types, providing quantitative insights into model performance. Additionally, the scope is extended in the Discussion to address broader themes essential for clinical translation, such as dataset biases, challenges in implementation, emerging trends (e.g., federated learning and multimodal approaches), ethical and privacy concerns, and real-time deployment in resource-limited settings.

This paper is structured as follows: Part II presents the systematic review methodology, detailing the search, selection, and evaluation processes for the included studies. Part III provides a comprehensive analysis of the results, including lesion distribution, deep learning methods with analyses on the strengths and weaknesses of detection approaches at different stages, variations, datasets, preprocessing techniques, augmentation steps, evaluation metrics and meta-analysis. Part IV discusses broader challenges and opportunities for clinical adoption, including dataset biases, challenges in implementing deep learning in real-world clinical settings, emerging trends such as federated learning and multimodal approaches, ethical and privacy considerations, and real-time deployment in resource-limited environments. Finally, Part V concludes the paper by summarizing the key findings, addressing the research questions, and highlighting future directions for deep learning-based lesion detection in endoscopy and laparoscopy.

## II. SYSTEMATIC REVIEW METHOD

The systematic literature review gathered data from three main databases: IEEE, PubMed, and ScienceDirect, using the PRISMA model as the methodological foundation. Data search was conducted using a set of relevant keywords, including “(detection OR localization) AND (lesion OR lesions) AND (deep learning OR computer vision) AND (laparoscopy OR endoscopy)”. With this approach, the study aimed to obtain comprehensive and verified information related to the development of lesion detection technology in the context of endoscopy and laparoscopy, as well as the application of deep learning and computer vision techniques in this field. In the data search, we applied filters for papers published between 2019 and 2024.

The paper selection process was carefully conducted to ensure inclusion and exclusion criteria aligned with the research objectives. Included studies were full-text, English-language articles focusing on detection techniques that primarily utilized endoscopy and laparoscopy as modalities. On the exclusion side, papers were excluded based on the following criteria: non-English language, reviews, clinical trials or cohort studies lacking a focus on deep learning, irrelevant conference proceedings, and studies without relevance to deep learning methodology. Additional exclusion criteria included papers focusing on modalities other than endoscopy and laparoscopy, detection tools rather than lesions, and

VR/AR or machine learning applications not specifically centered on deep learning.

<figure><img src="image_2.png" /><figcaption>FIGURE 1. The systematic review procedure using PRISMA.</figcaption></figure>

The search yielded 979 articles from the three databases used, namely PubMed with 218 articles, ScienceDirect with 690 articles, and IEEE with 71 articles. The detailed search process can be seen in Fig. 1. After removing duplicate articles, the number reduced to 888 articles. The next selection process involved elimination based on titles, resulting in a reduction of 355 articles, leaving 533 articles for further examination. From the abstracts retrieved, 50 articles were selected for further review, with 35 articles meeting the inclusion criteria for analysis in this paper. All the papers included in this study focus on the endoscopy modality. Papers related to laparoscopy were excluded because they addressed tool detection rather than lesion detection, resulting in no laparoscopy studies being among the final 35 papers reviewed.

To provide a comprehensive discussion on emerging trends in lesion detection, an additional targeted search was conducted. This search identified 11 relevant papers focusing on federated learning, lightweight models, multimodal approaches, and few-shot learning, which were selectively included in the Discussion to ensure a thorough exploration of these important topics.

## III. RESULT

This section discusses key aspects related to the aforementioned RQ, including lesion detection across body tracts, deep
<table><caption>TABLE 1. The selected studies of deep learning and variation.</caption><thead><tr><th>Anatomical Category</th><th>Lesion Type</th><th>Dataset</th><th>Model(s)</th><th>variation</th><th>Result</th><th>Author</th></tr></thead><tbody><tr><td rowspan="12">Gastro intestinal Tract</td><td>Gastric lesions</td><td>Private</td><td>ResNet50, EfficientNetB0</td><td>Pretrained model</td><td>Acc=0.988, Sens=0.9038, Spec=0.9322, F1-score=0.9236, AUC=0.9654</td><td>Lee et al. [19]</td></tr><tr><td>Celiac disease</td><td>Private</td><td>ResNet50 Inception-v3</td><td>-</td><td>Acc=0.9594, Sens=0.972, Spec=0.9563</td><td>X. Wang et al. [20]</td></tr><tr><td>Polypoid lesion</td><td>Public</td><td>YOLOv8</td><td>-</td><td>Prec=0.98, Rec=0.979, Dice=0.9794</td><td>Sahafi et al. [21]</td></tr><tr><td>Gastric lesions</td><td>Private</td><td>YOLOv7</td><td>attention module</td><td>Prec=0.72, Rec=0.69, F1-score=0.71, mAP=0.71</td><td>S. Ahmad et al. [22]</td></tr><tr><td>Submucosal lesion, Esophagitis, Leukoplakia Polyp</td><td>Private</td><td>YOLOv5</td><td>-</td><td>Acc=0.929, Sens=0.919, Spec=0.947, PPV=0.970, NPV=0.864</td><td>Meng et al. [23]</td></tr><tr><td>Colorectal lesion</td><td>Private</td><td>YOLOv5</td><td>Pretrained model</td><td>The Etis-Larib Dataset: Map=0.8293, Prec=0.8144, Rec=0.7596; The CVC-Clinicdb Dataset: Map=0.9096, Prec=0.8865, Rec=0.8762, F1 Score=0.8823, F2 Score=0.8786</td><td>Carrinho & Falcão [24]</td></tr><tr><td>Colorectal lesion</td><td>Private</td><td>YOLOv7</td><td>attention module</td><td>AP Value: Polyps=0.923, Adenomas= 0.955, Cancer =0.87, Map@50=0.916</td><td>Gao et al. [25]</td></tr><tr><td>Telangiectasias, Ulcers, and Polyps lesion</td><td>Public</td><td>YOLOv5</td><td>attention module</td><td>Rec=0.923, mAP@0.5=0.959</td><td>Gao et al. [26]</td></tr><tr><td>Colorectal polyp lesion</td><td>Public</td><td>YOLOv5</td><td>-</td><td>Acc=0.77, Prec=0.68, Rec=100, F-Measure=0.81, PTPI=0.021 s</td><td>T. Ito et al. [27]</td></tr><tr><td>Gastric cancer lesion</td><td>Public</td><td>YOLOv4</td><td>Optimization algorithm</td><td>Prec=0.9272, Rec=0.7701, Map@0.5=0.9060</td><td>Karaman et al. [28]</td></tr><tr><td>Gastric and Intestine cancers</td><td>Public & Private</td><td>YOLOX</td><td>Pretrained model</td><td>Sens=0.5481±0.2655, Spec=0.64.20±0.0873, PPV=0.5719±0.1084, NPV=0.6241±0.1301</td><td>S. Nóbrega et al. [29]</td></tr><tr><td>Gastric lesion</td><td>Private</td><td>CNN, RCNN</td><td>attention module</td><td>Colonoscopy: Cvc-Clinic: Prec=0.9972, Rec=0.9721, F1=0.9844; ETIS-LARIB: Prec=0.9091, Rec=0.8333, F1=0.8695; Gastroscopy: Prec=0.6979, Rec=0.6528, F1=0.6713, Gastroscopy Video: Prec=0.9936</td><td>Zhang et al. [30]</td></tr><tr><td>Esophageal lesions</td><td>Private</td><td>Faster R-CNN, VGG16, UNET</td><td>-</td><td>Sens=0.962, Spec=0.762, PPV=0.16, NPV=0.997, Acc=0.771</td><td>Xia et al. [31]</td></tr><tr><td>Colon lesions</td><td>Private</td><td>DLGNet</td><td>attention module</td><td>Classification: Sens=0.9034, Spec=0.9718, Acc= 0.9628; Segmentation: Sens=0.8018, Spec=0.9655, Acc= 0.9462</td><td>Wu et al. [32]</td></tr><tr><td>Esophageal lesion</td><td>Private</td><td>CLELNet</td><td>-</td><td>Acc=0.9150</td><td>K.-N. Wang et al. [33]</td></tr><tr><td>Crohn's disease lesions</td><td>Private</td><td>ResNet-50</td><td>Pretrained model</td><td>Classification: IOU=0.9596, Prec=0.9604, Sens=0.9539, Spec=0.9773, NPV = 0.9793; Segmentation: IOU=0.6566, Dice=0.7808 Patient Split: Acc=0.9838, Sens=0.9572, Spec=0.9977; Random Split: Acc=0.9858, Sens=0.9621, Spec=1</td><td>Tang et al. [34]</td></tr><tr><td>Angiectasia, lymphangiectasia, GI bleeding, and Ulcer Small bowel lesion</td><td>Public</td><td>CNN</td><td>-</td><td>Acc=0.99, Sens=0.996, Spec=0.9986</td><td>Majtner et al. [35]</td></tr><tr><td>Colorectal lesion</td><td>Private</td><td>YOLOv3</td><td>-</td><td>Sens=0.9917, Spec=0.9992, Acc=0.9986 Image Procs Time Of Model=48 ms/Image Of The Physicians=0.40±0.24 s/Image</td><td>Bajhaiya & Narayanan Unni [36]</td></tr><tr><td>Colon cancer</td><td>Public</td><td>YOLOv3-MSF</td><td>Pretrained model</td><td>AUC=0.983, Sens=0.946, Spec=0.952, PPV=0.688, NPV=0.994, Acc=0.951</td><td>Matsui et al. [38]</td></tr><tr><td>Pre-colorectal cancerous abnormalities</td><td>Public & Private</td><td>SSD, YOLOv4, YOLOv5</td><td>Pretrained model</td><td>Acc=0.9604, Prec=0.9398, Rec=0.9487, F-Measure=0.9402</td><td>Murugesan et al. [39]</td></tr><tr><td>Colorectal polyp lesion</td><td>Public & Private</td><td>Mask R-CNN</td><td>attention module</td><td>Prec=0.99071, Rec=0.98064, mAP=0.988</td><td>Aliyi et al. [40]</td></tr><tr><td></td><td></td><td></td><td></td><td>Prec=0.949, Rec=0.969, F1-score=0.959, F2-score=0.965</td><td>Yang et al. [41]</td></tr></tbody></table>
<table><caption>TABLE 1. (Continued.) The selected studies of deep learning and variation.</caption><tbody><tr><td></td><td>Ulcerative colitis lesion</td><td>Private</td><td>UCFNNet</td><td>Attention module</td><td>Private DB: ACC=0.8957, MCC=0.8552, Li et al. Prec=0.8926, Rec=0.8948, F1-score=0.8978; [42] Public DB: ACC=0.8547, MCC = 0.8042, Prec=0.8562, Rec=0.8400, F1-score=0.8453 Detection: Acc=0.77; Classification: Acc= X. Gao et al. 0.84; Segmentation: Acc=0.63 [43]</td></tr><tr><td></td><td>Squamous pre-cancers</td><td>Private</td><td>YOLOv3, mask-RCNN</td><td>-</td><td>Acc=Up To 0.9456</td></tr><tr><td></td><td>Colorectal lesion</td><td>Public &amp; Private</td><td>RetinaNet</td><td>-</td><td>W. Sun et al. [44]</td></tr><tr><td></td><td>Gastric cancer lesion</td><td>Private</td><td>Mask R-CNN</td><td>-</td><td>Z. -H. Cui et al. [45]</td></tr><tr><td rowspan="2">Respiratory Tract</td><td rowspan="2">Laryngeal cancer lesions</td><td rowspan="2">Private</td><td>MobileNetV2, EfficientNet, DenseNet121</td><td rowspan="2">Ensemble</td><td>R. Bhattacharjee et al. [46]</td></tr><tr><td></td><td></td></tr><tr><td rowspan="2">NPC (Nasopharyngeal carcinoma) lesions</td><td rowspan="2">Laryngeal carcinoma</td><td>Private</td><td>YOLOv8</td><td rowspan="2">Ensemble</td><td>Prec=0.977, Rec=0.943, Map=0.977, F1-score=0.96 He et al. [47]</td></tr><tr><td>Public &amp; Private</td><td>YOLOv5</td><td>Vocal Cord Carcinoma: Sens=0.71 – 0.78; Wellenstein et al. Benign Vocal Cordlesions: Sens=0.70 – 0.82 [48]</td></tr><tr><td rowspan="2">Urinary Tract</td><td>Bladder cancer lesions</td><td>Private:</td><td>DCNN</td><td>Pretrained model</td><td>ROC=0.98, J=0.837, Sens=0.897, Spec=0.94 Ikeda et al. [49]</td></tr><tr><td>Upper urinary tract (UUT) lesion</td><td>Private:</td><td>The single shot multibox detector (SSD)</td><td>-</td><td>Acc=0.902, Resectoscopy: Sens=0.84; Liu et al. Ureteroscopy: Sens=0.922, UO Tracking Performance>0.95 [50]</td></tr><tr><td rowspan="3">Reproduc tive Tract</td><td>Cervical lesion</td><td>Private</td><td>DenseNet</td><td>Pretrained model</td><td>Acc=73.08, AUC=75, T. Zhang et al. [51]</td></tr><tr><td>Cervical squamous intraepithelial lesions</td><td>Private:</td><td>Mask R-CNN,</td><td>-</td><td>Sens=0.8538, Spec=0.8262, Acc=0.8410, AUC=0.93, Rec=0.8473, DICE=0.6164, Average Acc=0.9559 Yuan et al. [52]</td></tr><tr><td>cervical lesions</td><td>N/A</td><td>Faster Region-Based Convolutional Neural Network (FR-CNN)</td><td>-</td><td>Acc=0.99 Elakkiya et al. [53]</td></tr></tbody></table>

learning methodologies, variations in deep learning, dataset characteristics, and metrics.

From the 35 full-text articles obtained, we extracted data and organized it into two tables (Table 1 and Table 2) due to spatial constraints and the need for focused RQ discussion. Table 1 focuses on the selected studies of deep learning models and their variations. In this table, we present a discussion related to the types of lesions, the datasets source (public or private), the types of deep learning methods used, what variations are carried out by researchers such as adding modules or using pretrained weights or using optimization and many others. There is also a metric column which is no less important to discuss. While Table 1 and Table 2 share information on lesion types and datasets. However, Table 2 discusses in more depth the collection of datasets taken from patients and includes details on resolution, dataset size (number of images and utilization details), preprocessing methods, and augmentation operations. We collect these two tables to answer and handle our RQ so that it is in accordance with the focus of our research.

Fig. 2 provides a conceptual diagram of our review, illustrating the key discussion points aligned with our six research questions (RQs) to enhance readability. This diagram contains six icons where each icon represents our RQ. The first icon on the far left represents RQ1, related to the types of lesions detected based on the keywords we defined, along with their classification into four categories of body tracts. The second icon represents RQ2, which addresses the deep

learning methods used for lesion detection. The third icon represents RQ3, which concerns the variations and modifications applied to deep learning methods for lesion detection. The fourth icon represents RQ4, focusing on the datasets used in lesion detection research. The fifth icon corresponds to RQ5, which addresses the preprocessing and augmentation techniques used in lesion detection studies. Finally, the sixth icon represents RQ6, which deals with the metrics employed in lesion detection research.

## A. LESION ACROSS BODY TRACT

From the articles we reviewed, we categorized lesion locations in the body into four main categories: the digestive tract [54], urinary tract [55], respiratory tract [56], [57], and reproductive tract [58]. Based on the findings from these articles, we did not identify any endoscopic lesions in other parts of the body such as the brain, joints, or abdomen. The majority of papers focus on the gastrointestinal tract, with a total of 27 papers. The distribution of papers across the four categories is shown in Fig. 3. The remaining 8 papers cover the other three categories: 2 papers discuss the urinary tract, 3 papers address the reproductive tract, and the remaining 3 papers focus on the respiratory tract. Treatment.

### 1) GASTROINTESTINAL TRACT

The digestive system is one of the vital systems in the human body, responsible for the digestion of food [59]. Its functions include processing ingested food, absorbing nutrients, and
expelling undigested food waste from the body [60]. The digestive tract, also known as the gastrointestinal tract, extends from the mouth to the anus and includes several key organs such as the rectum, large intestine, small intestine, and stomach [61]. The digestive tract hosts approximately 1,000 different types of bacteria [62] that play significant roles in metabolism, health, and immunity. Additionally, the digestive system produces various hormones that facilitate digestion, such as ghrelin, cholecystokinin, gastrin, among others [63].

Similar to other parts of the body, the gastrointestinal tract is susceptible to various diseases. Lifestyle is a critical factor influencing overall health; poor lifestyle choices can lead to injuries or abnormalities, including lesions [64]. These lesions may manifest as polyps, ulcers, or neoplasms. Early detection is therefore essential to ensure timely treatment and to prevent the progression of the disease [65], which, if untreated, could become life-threatening. The use of endoscopy in examining the digestive tract has significantly improved the early detection of diseases and cancers, thereby contributing to the reduction of mortality rates.

Various types of lesions can occur in the gastrointestinal tract. For example, in the esophagus, changes due to chronic acid reflux can occur, leading to lesions such as esophageal ulcers and Barrett's esophagus. Numerous studies in the field of artificial intelligence (AI) have been conducted to address these issues. Wu et al. [32] performed classification and segmentation of esophageal lesions, while Tang et al. [34] focused on detecting the types and regions of esophageal lesions. These studies are undoubtedly beneficial for the medical field. Gastrointestinal lesions are not confined to the esophagus; they can also occur throughout the digestive tract, including the small intestine, large intestine, and stomach. Research on gastric lesion detection has been carried out by several researchers [19], [22], [31]. Employing both one-stage and two-stage methods, achieving satisfactory accuracy in each. Early detection of gastric lesions can prevent the development of gastric cancer before it progresses to a more severe stage. Furthermore, research has been conducted on esophageal lesions, such as esophageal squamous cell carcinoma (ESCC) [23]. ESCC ESCC represents another type of cancer that poses a severe risk to patient survival if not treated promptly. Another dangerous type of lesion, and a leading cause of death worldwide, is colorectal cancer. Several studies on colorectal issues, including the detection of lesions [25], [38], polyps [28], and precancerous conditions [40], have been conducted. Research focusing on the intestines has been carried out to detect small lesions in ulcerative colitis [27] and classify and detect small bowel lesions [37] in the small intestine. Advances in technology have made it easier to identify these issues. The detection of various types of lesions in the digestive tract, such as telangiectasia, ulcers, and polyps, has become an important topic of research [27]. Similarly, the classification and localization of GI diseases such as angiectasia, lymphangiectasia, GI bleeding, and ulcers have also been carried out to assist in patient diagnosis [43].

Detection of precancerous and cancerous lesions was also conducted, including Squamous Pre-Cancers detection [43], gastric cancer detection [45], and intestine cancer detection [30]. All of these efforts are crucial steps in reducing mortality rates and improving the quality of life for patients with gastrointestinal diseases. These studies represent a significant step in understanding and managing lesions in the gastrointestinal tract organs, which can make a crucial contribution to medical diagnosis and treatment.

## 2) RESPIRATORY TRACT

The respiratory tract is responsible for facilitating gas exchange within the body, specifically oxygen intake and carbon dioxide removal [67]. This system ensures that oxygen is delivered to support metabolic activities, while carbon dioxide is expelled to maintain proper bodily function. The respiratory system is divided into upper and lower tracts with the upper tract comprising the nose, nasal cavity, pharynx, and larynx, and the lower tract consisting of the trachea, bronchi, and bronchioles [68].

Just like the gastrointestinal system, the respiratory tract is vulnerable to medical conditions, leading to lesions due to factors such as chronic inflammation, environmental pollutants, and infections [69]. Research on detecting lesions in the respiratory system is essential for diagnosing critical conditions like nasopharyngeal carcinoma, laryngeal carcinoma [48], and vocal cord lesions. One study even highlights the real-time detection of nasopharyngeal carcinoma using deep learning, improving early detection accuracy [47]. Technological advancements, particularly in deep learning, have significantly improved respiratory tract research outcomes, improving patients' survival and recovery.

## 3) URINARY TRACT

The urinary tract is a system in the body that has a critical function, maintaining body fluid balance. This system, which is responsible for producing, storing, and excreting urine, has several main components: the kidneys, ureters, bladder, and urethra [70]. Like other bodily systems, the urinary tract is vulnerable to abnormalities, increasing the likelihood of lesion formation. Among these are bladder cancer lesions, which have been the focus of deep learning research in this study [49].

In particular, research related to the urinary tract has focused on detecting upper urinary tract (UUT) lesions [50] in real time. This study used the Single-Shot Multibox Detector (SSD) method with 447 endoscopy images for training and 818 images for testing. The results show a high level of accuracy. The success of this detection system has significant implications for urological healthcare, supporting urologists by enhancing diagnostic accuracy and aiding in clinical decision-making.

## 4) REPRODUCTIVE TRACT

The reproductive system is responsible as the primary biological mechanism underlying human reproduction. In women, the main components include the ovaries, uterus, and
<figure><img src="image_3.png" /><figcaption>FIGURE 2. Diagram representing research questions and key components of SLR.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>FIGURE 3. Endoscopic lesion studies by body system.</figcaption></figure>

fallopian tubes [71], while in men, they include the testes and sperm ducts [72]. Colposcopy [73] is a female reproductive examination procedure that is usually used when the results of a Pap smear lab show abnormalities and further examination is needed. This procedure is very important for monitoring women's reproductive health.

Research related to AI, especially deep learning, has also been widely developed for the detection and classification of cervical colposcopy images. One study [52] detected high-grade squamous intraepithelial lesions (HSIL). HSIL is a precancerous condition that certainly requires serious treatment. This condition indicates a potential progression to cervical cancer. As AI continues to develop, it has become easier to analyze colposcopy images, allowing for more accurate early detection. This is highly beneficial in assisting doctors with making clinical decisions for their patients.

Studies have conducted the detection and classification of three types of cervical cancer lesions: squamous cell cancer, adenocarcinoma, and small cell cancer [51], [53]. Because

each type of cancer lesion has different characteristics, they require different treatments. For this reason, it is necessary to develop a model that can recognize and distinguish these three types of cancer lesions. By using technology and AI, this study obtained results with high accuracy. Early diagnosis of this disease by detecting lesions before they reach a high level of severity will increase the chances of survival for patients.

## B. DEEP LEARNING

Deep learning is a branch of machine learning [74] that has revolutionized how we understand and analyze data. It mimics human thought processes by utilizing complex artificial neural networks that resemble the structure of the human nervous system [75]. These networks consist of multiple layers that gradually process information, allowing the system to learn complex and abstract patterns from data [74]. Each layer processes input and passes it to the next, enabling the system to identify patterns and make decisions. The input data can be images, text, videos, or structured formats like CSV files, which flow through interconnected layers where each neuron receives input from the previous layer.

Advances in deep learning have significantly enhanced object detection, driving its growing popularity. In fact, deep learning-based methods have become the primary focus in object detection research [76]. There are two main approaches to deep learning-based object detection: the one-stage and the two-stage approaches [77]. The one-stage approach involves a single process where the steps of detection and classification are carried out in one go. Because it involves only one process, the one-stage approach is faster compared to the two-stage approach. However, the one-stage
<table><caption>TABLE 2. The selected studies of dataset, preprocessing and augmentation.</caption><thead><tr><th>Author</th><th>Lesion</th><th>Dataset</th><th>Number of Patients</th><th>Number of images</th><th>Resolution</th><th>Preprocessing</th><th>Augmentation</th></tr></thead><tbody><tr><td>Lee et al. [19]</td><td>Gastric lesions</td><td>Private</td><td>1902</td><td>19,518</td><td>224 × 224</td><td>N/A</td><td>No</td></tr><tr><td>X. Wang et al. [20]</td><td>Celiac disease</td><td>Private</td><td>8</td><td>2140</td><td>576x576</td><td>Resize to 512×512, remove text and black frames box</td><td>N/A</td></tr><tr><td>Sahafi et al. [21]</td><td>Polypoid lesion</td><td>Public</td><td>N/A</td><td>2352</td><td>360x360</td><td>N/A</td><td>N/A</td></tr><tr><td>S. Ahmad et al. [22]</td><td>Gastric lesions</td><td>Private</td><td>N/A</td><td>61734</td><td>950×1120</td><td>N/A</td><td>Yes</td></tr><tr><td>Meng et al. [23]</td><td>Submucosal lesions, Esophagitis, and Leukoplakia</td><td>Private</td><td>1160</td><td>6142</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>Carrinho & Falcao [24]</td><td>Polyp</td><td>Public</td><td>N/A</td><td>N/A</td><td>388×288 - 1920×1080</td><td>Resize to 320×224</td><td>Yes</td></tr><tr><td>Gao et al. [25]</td><td>Colorectal lesions</td><td>Private</td><td>N/A</td><td>1709</td><td>420×389×3</td><td>HSV color enhancement</td><td>Yes</td></tr><tr><td>Gao et al. [26]</td><td>Colorectal lesions</td><td>Private</td><td>N/A</td><td>1709</td><td>420×389×3</td><td>Adaptive histogram equalization</td><td>Yes</td></tr><tr><td>T. Ito et al. [27]</td><td>Telangiectasias, Ulcers, and Polyps lesions</td><td>Public</td><td>N/A</td><td>5,657</td><td>640 x 640</td><td>Resize to 640x640</td><td>Yes</td></tr><tr><td>Karaman et al. [28]</td><td>Colorectal polyp lesion</td><td>Public</td><td>40</td><td>3433</td><td>N/A</td><td>Image cleaning</td><td>Yes</td></tr><tr><td>S. Nóbrega et al. [29]</td><td>Gastric cancer lesions</td><td>Public</td><td>N/A</td><td>N/A</td><td>720×576 & 768×576</td><td>Resize to 416×416</td><td>N/A</td></tr><tr><td>Z. Zhang et al. [30]</td><td>Gastric and Intestine cancers</td><td>Public &private</td><td>N/A</td><td>3074</td><td>640×640</td><td>Crop text from clinical reports, extract video frames</td><td>Yes</td></tr><tr><td>Xia et al. [31]</td><td>Gastric lesions</td><td>Private</td><td>797</td><td>1,023,955</td><td>N/A</td><td>N/A</td><td>Yes</td></tr><tr><td>Wu et al. [32]</td><td>Esophageal lesions</td><td>Private</td><td>748</td><td>1051</td><td>512x512</td><td>Normalization, specular reflection removal</td><td>Yes</td></tr><tr><td>K.-N. Wang et al. [33]</td><td>Colon lesions</td><td>Private</td><td>N/A</td><td>3568</td><td>512×512</td><td>Grayscale cropping and resize to 512×512</td><td>Yes</td></tr><tr><td>Tang et al. [34]</td><td>Esophageal lesions</td><td>Private</td><td>319</td><td>1003</td><td>512×512</td><td>N/A</td><td>N/A</td></tr><tr><td>Majtner et al. [35]</td><td>Crohn's disease lesions</td><td>Private</td><td>38</td><td>7744</td><td>N/A</td><td>Text removal, contrast enhancement, histogram equalization, gradient X, dehazing</td><td>Yes</td></tr><tr><td>Bajhaiya & Narayanan Unni [36]</td><td>Angiectasia, lymphangiectasia, GI bleeding, and Ulcer</td><td>Public</td><td>N/A</td><td>3658</td><td>336x336</td><td>Resize to 224x224</td><td>Yes</td></tr><tr><td>R.-Y. Zhang et al. [37]</td><td>Small bowel lesions</td><td>Private</td><td>701</td><td>111861</td><td>N/A</td><td>Decompose images into R, G, and B channels</td><td>N/A</td></tr><tr><td>Matsui et al. [38]</td><td>Colorectal lesions</td><td>Private</td><td>N/A</td><td>6531</td><td>N/A</td><td>N/A</td><td>Yes</td></tr><tr><td>Murugesan et al. [39]</td><td>Colon cancer</td><td>Public</td><td>N/A</td><td>1000</td><td>N/A</td><td>Noise reduction, black frame removal, color equalization, hair removal, Gaussian filter, color space translation</td><td>Yes</td></tr><tr><td>Aliyi et al. [40]</td><td>Pre-colorectal cancerous abnormalities</td><td>Public &private</td><td>N/A</td><td>8.000</td><td>320x320</td><td>Standardize image formats</td><td>Yes</td></tr><tr><td>Yang et al. [41]</td><td>Colorectal polyp lesion</td><td>Private</td><td>1197</td><td>3920</td><td>N/A</td><td>Resize and normalization</td><td>Yes</td></tr><tr><td>Li et al. [66]</td><td>Ulcerative colitis lesions</td><td>Public &private</td><td>N/A</td><td>6692</td><td>N/A</td><td>N/A</td><td>Yes</td></tr><tr><td>X. Gao et al. [43]</td><td>Squamous pre-cancers</td><td>Private</td><td>N/A</td><td>350</td><td>920x1080</td><td>Blur detection and artifact removal</td><td>N/A</td></tr><tr><td>W. Sun et al. [44]</td><td>Colorectal lesions</td><td>Private</td><td>1367</td><td>1,709</td><td>375x347</td><td>N/A</td><td>N/A</td></tr><tr><td>Z.-H. Cui et al. [45]</td><td>Gastric cancer lesions</td><td>Public &private</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td><td>N/A</td></tr><tr><td>R. Bhattacharjee et al. [46]</td><td>Laryngeal cancer lesions</td><td>Private</td><td>30</td><td>3000</td><td>N/A</td><td>Adjust contrast and brightness, remove blank frames</td><td>Yes</td></tr></tbody></table>
<table><caption>TABLE 2. (Continued.) The selected studies of dataset, preprocessing and augmentation.</caption><tr><td>He et al. [47]</td><td>NPC (Nasopharyngeal carcinoma) lesions</td><td>Private</td><td>690</td><td>2,429</td><td>400x400</td><td>Image anonymization and randomization</td><td>Yes</td></tr><tr><td>Wellenstein et al. [48]</td><td>Laryngeal carcinoma</td><td>Public &amp;private</td><td>649</td><td>4488</td><td>448x448</td><td>Remove "Reinke's edema" labels, exclude small images, create ground truth bounding boxes</td><td>Yes</td></tr><tr><td>Ikeda et al. [49]</td><td>Bladder cancer lesions</td><td>Private</td><td>109</td><td>2102</td><td>1350x1080</td><td>N/a</td><td>Yes</td></tr><tr><td>Liu et al. [50]</td><td>Upper urinary tract (UUT) lesions</td><td>Private</td><td>51</td><td>3523</td><td>300 × 300</td><td>Resize image</td><td>Yes</td></tr><tr><td>T. Zhang et al. [51]</td><td>Cervical lesions</td><td>Private</td><td>N/A</td><td>N/A</td><td>N/A</td><td>Resize to 224x224</td><td>Yes</td></tr><tr><td>Yuan et al. [52]</td><td>Cervical squamous intraepithelial lesions</td><td>Private</td><td>N/A</td><td>N/A</td><td>512x512</td><td>Resizing, encode text information</td><td>Yes</td></tr><tr><td>Elakkiya et al. [53]</td><td>cervical lesions</td><td>N/A</td><td>1,993</td><td>3105</td><td>100×100</td><td>Resize, crop, recolor, flatten</td><td>Yes</td></tr></table>

approach also has drawbacks, particularly in terms of accuracy when detecting small objects [78]. In terms of accuracy, the two-stage approach generally outperforms the one-stage approach [79]. Within the one-stage approach, several algorithms are commonly used. One of the most popular methods is YOLO (You Only Look Once) [80]. Various versions of YOLO have been developed, ranging from YOLOv1 to YOLOv10 [81], and there are also many other versions that have been developed, such as YOLOX, YOLOR, and others [82]. Several studies on lesion detection using YOLO have been conducted by these studies [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [39], [47], [48]. Besides YOLO, there is also the SSD (Single Shot MultiBox Detector) method. Unlike YOLO, SSD provides different scales to handle objects, thereby increasing accuracy and producing more precise results [83]. This makes SSD a reliable candidate for detection methods, rivaling YOLO. SSD has been used for research in detecting pre-colorectal cancerous abnormalities and in the upper urinary tract [40], [50]. Another method worth mentioning is RetinaNet. RetinaNet excels in detecting small objects and is particularly suitable for handling imbalanced classes during training [84]. This method has been employed for detecting colorectal lesions [44].

In addition to one-stage approach, object detection also employs a sequential two-stage approach, comprising region proposal generation and object classification [85]. Using these two-stage processes has the advantage of a higher level of accuracy when compared to the one-stage approach. However, this two-stage approach also has disadvantages. Because it uses two processes, the processing time required is longer compared to the one-stage approach. Therefore, in real-time applications, the one-stage approach is often preferred because it is lighter and faster than the two-stage approach. However, if the consideration is to find more accurate detection results, then the choice will fall on the two-stage approach. In this two-stage approach, there are also several models that are commonly used. Among them is R-CNN (Region-based Convolutional Neural Networks) [86]. Research on lesion detection using R-CNN is presented in this study [31]. This R-CNN is an early version of the

two-stage approach model. However, due to its weakness in terms of speed, a faster version of this R-CNN was created, namely Fast R-CNN [87]. The difference is that Fast R-CNN is faster because there is a combination of several steps into one more efficient process. Further developments led to the introduction of Faster R-CNN, which improved efficiency and accuracy. Faster R-CNN distinguished itself from the previous version by employing the selective search method. In Faster R-CNN, there is an important component called the Region Proposal Network (RPN) [88]. This RPN integrates the process of generating region proposals directly into the main network, enhancing efficiency and speeding up the object detection process. Lesion detection research using Faster R-CNN is the study [32] which detects esophageal lesions and study [51] detects cervical lesions. A further development version is Mask R-CNN combines Faster R-CNN for target detection with Fully Convolutional Network (FCN) for semantic segmentation [89]. Research using Mask R-CNN is also widely used for lesion detection, including research for the detection of colorectal polyp lesions [41], squamous pre-cancers [43], gastric cancer lesions [45], and cervical squamous intraepithelial lesions [52].

The choice between one-stage and two-stage object detection models depends heavily on the specific demands of the task, particularly in medical imaging contexts such as colorectal cancer (CRC), squamous cell carcinoma (SCC), gastric lesions, and polypoid lesions. One-stage detectors, such as YOLO and SSD, excel in speed and computational efficiency, making them ideal for real-time applications. For instance, YOLOv5 demonstrated remarkable performance in CRC detection with nearly perfect metrics—precision (99.071%), recall (98.064%), and mAP (98.8%)—allowing clinicians to analyze colonoscopy videos efficiently during live procedures [40]. SSD also performed well in CRC detection, achieving precision (90.42%) and recall (91.32%), though its mAP (87.5%) was slightly lower than YOLO-based methods [40]. Meanwhile, YOLOv8 showed adaptability to polypoid lesions, with optimized anchor boxes and loss functions enabling it to detect lesions of varying sizes, a critical feature for gastrointestinal imaging [21]. YOLOv7
and CSS-YOLOv7 further enhance the potential for real-time deployment with their balance of speed and efficiency [22], [26]. Refined-SSD also demonstrated high sensitivity and processing speed, highlighting its potential in real-time applications [50]. Additionally, dual-branch networks improve detection by combining global and local information, offering enhanced accuracy in certain contexts [33]. CLELNet introduced the ability to continuously learn new tasks without forgetting previous ones, making it particularly useful in clinical settings with evolving imaging needs [34]. Conversely, two-stage detectors like Mask R-CNN are known for their precision in detailed lesion segmentation. This is particularly useful in tasks such as gastric lesion detection, where segmentation accuracy (63%) is crucial for identifying ulcers or submucosal tumors [31]. Mask R-CNN also excels in detecting small targets with high accuracy, making it advantageous for identifying SCC or complex gastric conditions [45]. Another strength of two-stage models is their robustness in noisy environments. For instance, the Refined-SSD model demonstrates significant resilience in such conditions, achieving a high sensitivity of 0.922 on ureteroscopy video frames despite challenges posed by less detailed background information. This capability enables the model to effectively detect ureteral orifices (UOs) even when they are obscured or difficult to identify due to environmental noise, highlighting its suitability for medical imaging tasks requiring reliable detection under varying conditions [50]. Faster R-CNN also stands out for its ability to detect multiple targets with high sensitivity, making it well-suited for medical imaging tasks that require multi-target classification [31].

Despite their respective strengths, both one-stage and two-stage detectors present areas where further refinement could yield significant improvements. One-stage detectors, while highly efficient, are typically limited in generating pixel-level segmentation masks. Detecting gastric lesions presents challenges due to the complex gastric environment and the similarity of features between different lesions. While methods like Faster R-CNN effectively use bounding boxes for lesion detection, addressing these complexities may require further enhancement of feature extraction and localization techniques [31]. YOLOv3 demonstrates a speed advantage (10–15 fps) in SCC detection and is effective in generating bounding boxes for real-time applications. However, as noted in Paper 43, the bounding boxes generated by YOLOv3 do not provide pixel-level information, which may limit its ability to precisely localize lesions requiring detailed segmentation, such as suspected SCC regions where biopsies must be collected from specific spots [43]. While YOLOv5 has demonstrated effectiveness in real-time applications, identifying small objects remains a recognized challenge for many one-stage detectors in medical imaging, particularly in contexts like polyp detection. This general challenge may also extend to YOLOv5 in certain scenarios, though further evaluation would be required to confirm its specific performance [28]. Conversely, two-stage detectors, such as Mask R-CNN, excel in detailed segmentation but may face challenges

in speed and computational demands. For instance, Mask R-CNN processes frames at an average speed of 1.2 seconds per frame, which might be less suitable for real-time workflows compared to faster one-stage methods like YOLOv3 [43]. Similarly, Faster R-CNN has been used effectively for lesion detection and multi-target analysis, but its two-stage process may be less efficient for real-time applications compared to one-stage models. While the study did not explicitly discuss the scalability of two-stage detectors in resource-constrained settings, it highlighted challenges in training robust models due to the underrepresentation of rare lesion types, such as gastric cancer, which were excluded from the dataset because of the limited number of available patient samples (n = 4). This underscores the importance of broader and more diverse datasets for improving model performance in detecting rare lesion types [31]. Emerging models, such as transformer-based architectures and sECANet, offer promising features like high inference speeds (e.g., 80 FPS for transformers) and real-time efficiency, yet further exploration of their robustness and scalability in diverse clinical settings would enhance their applicability [30], [41].

### C. VARIATION IN DEEP LEARNING METHOD

The variations in deep learning models discussed in this paper involve modifications beyond the baseline methods. These variations include architectural changes such as adding modules, employing ensemble methods by combining multiple models, or utilizing pretrained weights and transfer learning. Such variations are commonly employed by researchers to enhance accuracy and address challenges in solving research problems. These adjustments are crucial for optimizing machine learning processes and improving accuracy, offering researchers with similar focuses and datasets new perspectives for developing more effective models.

#### 1) ARCHITECTURAL MODIFICATIONS

To enhance deep learning performance, architectural modifications can be made by adding or reducing the number of layers [90], [91]. These modifications may include convolutional layers, recurrent layers, or attention layers. Tailoring the model to the data's characteristics facilitates better feature extraction, aligning with research objectives. The ultimate goal is to improve detection accuracy or speed. In lesion detection, researchers have enhanced architectures by integrating attention mechanisms, which have proven to improve efficiency and accuracy [92]. For example, one study [25] conducted colorectal lesion detection using the YOLOv5 architecture with additional modules like the coordinate attention (CA) and ghost modules. Similarly, research [26] explored the YOLOv7 architecture with the CNeB module to better extract information from colorectal lesion images. Another study [33] added an attention guidance module and a dual-branch module to the YOLOv5 backbone, and a further study incorporated an efficient channel attention module into a convolutional block [41]. These modifications have
yielded satisfactory improvements in accuracy. Additionally, study [45] replaced the FPN with BiFPN in the Mask R-CNN architecture, enhancing the model's effectiveness by improving connectivity between various feature levels.

<figure><img src="image_5.png" /><figcaption>FIGURE 4. Comparison of the number of papers using private, public, and both datasets.</figcaption></figure>

## 2) PRETRAINED WEIGHTS

Pretrained weights [93], generated from previous training on large datasets like COCO [94] or ImageNet [95], are particularly useful when training data is limited. Models pretrained on these datasets have optimized weights for recognizing common features, mitigating issues associated with small datasets. Transfer learning and fine-tuning allow models to adapt to new datasets, significantly reducing training time. Studies utilizing pretrained models include [24] for detecting polyps with YOLOv4, [35] for Crohn's disease lesions, [40] for pre-colorectal cancer abnormalities, [49] for bladder cancer lesions, and [51] for cervical lesions. The study [51] also fine-tuned pretrained weights on the Kaggle [96] dataset. Moreover, pretrained weights from the COCO dataset were used in studies like [29] for gastric cancer lesions with YOLOv4 and [39] for colon cancer with YOLOv3.

## 3) ENSEMBLE MODELS

Ensemble models [97], which combine predictions from multiple models, have been explored to achieve more accurate results. For instance, a study on laryngeal cancer lesion detection used an ensemble of MobileNetV2, EfficientNetB0, and DenseNet121 [46], showing improved results compared to single models. Another study [48] combined YOLOv5s and YOLOv5m weights for laryngeal carcinoma detection, also demonstrating enhanced accuracy.

## 4) OPTIMIZATION ALGORITHMS

Despite the advancements, deep learning methods face challenges in determining optimal hyperparameters. Beginners often experiment with various parameters, which is time-consuming. Optimization algorithms can streamline this process. For example, the study on colorectal polyp lesion detection [28] used YOLOv5 and bee colony optimization (BCO), an algorithm inspired by bee behavior to find optimal solutions [98]. Modifications and variations in deep learning

models significantly enhance performance in lesion detection. Success is not only measured by accuracy but also by the model's ability to recognize patterns in medical images and adapt to the dataset. These advancements are beneficial for the medical field by improving lesion detection capabilities.

## D. DATASET

The dataset is a crucial component for the deep learning process. Even the performance of the constructed model is closely related to and influenced by the quality and representation of the dataset used. A dataset is more than a collection of data used to train a model. It is a source of information that will be used by the model to recognize patterns and make predictions. The quality and representativeness of a well-prepared dataset exert a profound impact on the model's efficacy [99], [100]. Conversely, a small and non-representative dataset can lead to overfitting, hindering model generalization [101]. Therefore, the model is very dependent on the dataset used in training.

In the medical field, datasets also play an important role as they are closely related to the ability of deep learning models to detect and diagnose diseases. There are various datasets commonly used in the medical world. Among them are clinical patient data, Magnetic Resonance Imaging (MRI), Computed Tomography (CT) scans, ultrasound images, endoscopy images, and many others [102]. Regardless of the form of the dataset, whether it is clinical data, images, or others, it should possess representative and diverse characteristics.

The lesion-related datasets analyzed were categorized into private and public types, as detailed in Table 2. The presence of these two types of datasets shows the variation in approaches used in research, both focused on specific data that is not publicly available and those that prioritize accessibility and transparency.

Research using private data often faces access limitations due to privacy and security concerns. This is often the case in medical research, where datasets contain sensitive information about patients and health conditions. Although not publicly accessible, the use of private data often provides more specific and in-depth results because it reflects more complex real-world situations. As shown in Fig. 4, studies using private datasets outnumber those using public datasets, with studies using both being the least common. One paper does not specify the source of its dataset, making it unclear whether it is public or private.

Publicly available datasets provide valuable resources for testing and validating research findings, while also promoting collaboration and reproducibility in research. In medical research, Public datasets alleviate accessibility constraints, broadening the scope of medical research and participation. We compiled the public datasets utilized in the analyzed papers in Table 3.

The public datasets found only covered two body parts: the gastrointestinal tract and the respiratory tract. The majority are related to the gastrointestinal tract, with a total of 13 pub-
<table><caption>TABLE 3. List of public datasets obtained from the collected papers.</caption><thead><tr><th>No</th><th>Dataset</th><th>Link</th></tr></thead><tbody><tr><td colspan="3" style="text-align: center; font-weight: bold;">Gastrointestinal tract</td></tr><tr><td>1</td><td>KID Dataset [21]</td><td>https://mdss.uth.gr/datasets/endoscopy/kid/</td></tr><tr><td>2</td><td>Etis larib [24]</td><td>https://polyp.grand-challenge.org/EtisLarib/</td></tr><tr><td>3</td><td>CVC ClinicDB [24]</td><td>https://gts.ai/dataset-download/cvc-clinicdb/</td></tr><tr><td>4</td><td>CVC-ColonDB [24]</td><td>http://pages.cvc.uab.es/CVC-Colon/index.php/databases/</td></tr><tr><td>5</td><td>Kvasir-SEG [24]</td><td>https://datasets.simula.no/kvasir-seg/</td></tr><tr><td>6</td><td>Kvasir-capsule [36]</td><td>https://github.com/simula/kvasir-capsule</td></tr><tr><td>7</td><td>Kvasir [44]</td><td>https://datasets.simula.no/kvasir/</td></tr><tr><td>8</td><td>Hyper Kvasir [40]</td><td>https://datasets.simula.no/hyper-kvasir/</td></tr><tr><td>9</td><td>Colonoscopic Dataset [24]</td><td>https://www.depeca.uah.es/colonoscopy_dset</td></tr><tr><td>10</td><td>SUN-Database [28]</td><td>http://amed8k.sundatabase.org/</td></tr><tr><td>11</td><td>PICCOLO [28]</td><td>https://www.biobancovasco.bioef.eus/en/Sample-and-data-catalog/Databases/PD178-PICCOLO-EN.html</td></tr><tr><td>12</td><td>GIANA [30]</td><td>N/A</td></tr><tr><td>13</td><td>EndoVisSub-Barret Grand Challenge [39]</td><td>https://endovissub-barrett.grand-challenge.org/Data_description/</td></tr><tr><td></td><td>the Faculty of Medicine, the University of Yamanashi (obtained on the Internet) [27]</td><td>N/A</td></tr><tr><td colspan="3" style="text-align: center; font-weight: bold;">Respiratory tract</td></tr><tr><td>1</td><td>Laryngoscope [8]</td><td>https://github.com/greenyin/Laryngoscope [8]</td></tr></tbody></table>

lic datasets identified. Some links are mentioned in the paper, while others are not, and the authors obtained those datasets from the internet. However, no public datasets were found for the urinary and reproductive tracts as mentioned in the paper.

## E. PREPROCESSING AND AUGMENTATION

Preprocessing and augmentation are two crucial stages in preparing data before being processed by deep learning models [103]. There are important steps that must be taken before the layers in a deep learning model can start processing data. Having a dataset doesn't mean it can be directly fed into the deep learning model. Two critical steps that need to be carried out first are preprocessing and augmentation. The purpose of preprocessing and augmentation is to enhance dataset quality, ultimately improving detection results. Preprocessing is essential for preparing the dataset to be free from noise, ensuring that all data is complete with no missing values, that input values are normalized, and that the data format is compatible with the model [104]. Noise, in this context, refers to irrele-

vant or unnecessary data that does not contribute to research objectives. Such data needs to be removed or excluded.

Another important step is data augmentation, which is crucial for making the dataset more varied and representative of the deep learning model [105]. A more varied dataset allows the model to learn from a wider range of examples, thereby improving its generalization ability. This will, of course, have a positive impact on detection results. Augmentation is just as important as preprocessing because it plays a key role in ensuring that the dataset is diverse. A diverse dataset enables the deep learning model to capture more comprehensive and better features, leading to improved detection results. The augmentation process typically includes transformations such as flipping, rotation, zooming, and shifting. Adding noise to image data can also enrich the features the model learns, enhancing detection outcomes [106].

Fundamentally, the main difference between preprocessing and augmentation lies in their objectives. Preprocessing aims to prepare raw data for further processing, while augmentation aims to enrich existing data variation by creating new variations from the existing data [103]. Therefore, sometimes preprocessing steps are included in augmentation by certain researchers if the process is intended to enrich the data.

Among the 35 papers reviewed, more than half employed preprocessing steps, highlighting its importance for improving accuracy. The discussion in this subsection will be divided into three main categories: data format and information handling, image enhancement, and augmentation:

### 1) DATA FORMAT AND INFORMATION HANDLING

Data format and information handling are crucial steps in data processing to ensure standardized and consistent data formats. Many research scenarios involve the use of multiple modalities, and different modalities mean different formats. This could pose a challenge if the datasets used in a single study have varying formats. Therefore, preprocessing is necessary to standardize the data format. The data format and information handling processes discussed are compiled from the 35 papers reviewed.

The first step is resizing data, which is part of preprocessing to standardize the data format, as discussed earlier. Images often need resizing not only to ensure uniformity but also to meet the specific size requirements of model architectures, such as 224 × 224 pixels for VGG and ResNet [107]. If the initial dataset sizes do not match this requirement, image resizing becomes necessary. Additionally, researchers sometimes need to reduce image sizes to lighten the computational load. Often, datasets derived from different modalities also have varying image sizes, making it necessary to standardize image sizes for further processing. Several studies in the papers we reviewed have performed image resizing during dataset preprocessing, including studies [20], [24], [27], [29], [33], [36], [50], [51].

Data format conversion is also carried out in some studies, such as [40], to ensure data consistency. Medical images often contain patient information, necessitating the removal
of identifying labels to protect privacy. Datasets with labels unrelated to the research objective should also be excluded. For example, Wallenstein et al. [48] removed labels with the text “Reinke’s edema” as they were irrelevant. Another study [47] anonymized images with patient identification data and randomized their order to protect privacy and reduce potential bias.

While preprocessing often involves removing unnecessary data, adding metadata may sometimes be necessary for research purposes [48]. For instance, image classification or annotation, such as bounding boxes or polygons, is required if the goal is to detect diseases or specific objects. This step can be skipped if using public datasets, which are generally ready for use. Study [40] involved image grouping, removing images that did not meet research specifications. Yuan et al. [52] extracted and encoded textual information from images for further analysis.

## 2) IMAGE ENHANCEMENT

Image enhancement techniques identified in the reviewed papers include color enhancement and detail enhancement. These processes are typically carried out to improve the quality and representation of images in datasets.

### a: COLOR ENHANCEMENT

This technique is used to improve the visual quality of images by adjusting the color properties. For example, Gao et al. [25] applied color adjustments in the HSV (Hue, Saturation, Value) color space to enhance image contrast and make colors more vivid. Another study [39] implemented automatic color correction and balancing to address color inconsistencies and ensure that colors are more realistic.

### b: DETAIL ENHANCEMENT

Some studies focus on enhancing image details by removing effects such as haze, which can obscure features and reduce image clarity. For instance, a study [35] removed haze effects and used gradient information to enhance edge contrast, resulting in sharper images. Another technique, Adaptive Histogram Equalization (AHE), was used in study [26] to improve local contrast and visibility of details in specific image areas. This technique was also applied in several other studies [39], [46], [47].

### c: NORMALIZATION

Standardizing the scale or range of features, known as normalization, is another method used to enhance image quality. Several studies employed normalization techniques [32], [41].

### d: NOISE REMOVAL

Removing noise is crucial for eliminating data unrelated to the study's objectives or data that could interfere with model processing. For example, specular reflections, which appear as bright white patches, can confuse the model by resembling tools or lesions. Study [32] addressed this issue by removing specular reflections. Additionally, artifacts from

image capture tools, such as vignetting effects (darker edges compared to the center), can result from lens limitations or improper lighting. Study [39] removed vignetting effects to improve image quality. Black frames, which can disrupt analysis, were also removed in studies [20], [39], [46]. Another study [37] decomposed images into color channels—R (red), G (green), and B (blue)—to enhance feature extraction capabilities of deep learning models, thereby improving detection accuracy.

## 3) AUGMENTATION

Image augmentation is a crucial process for increasing the diversity and variability of a dataset. Several methods of augmentation were identified in the reviewed papers:

### a: ROTATION

This technique involves rotating images at various angles to generate multiple versions of a single image. By applying different degrees of rotation, a dataset can be significantly enriched.

### b: FLIPPING

Images can be flipped horizontally or vertically to create new variations. This method helps the model learn from mirrored perspectives.

### c: SHEARING

Shearing alters the image's perspective by shifting it, resulting in a distorted view. This can provide the model with different angles and perspectives.

### d: TRANSLATION

Translation involves shifting the image to a different position within the frame. This method helps the model become more robust to positional changes.

### e: RESCALING

Changing the image size, known as rescaling, is another common augmentation technique. This method adjusts the dimensions of images, which can be important for standardizing inputs.

### f: BRIGHTNESS AND COLOR ADJUSTMENTS

Adjusting brightness, hue, saturation, and exposure levels of images helps in creating variations within the dataset. Studies [20] and [22] applied these techniques to enhance the diversity of their datasets.

### g: MOSAIC

Combining multiple images into a larger, more complex image, known as mosaic augmentation, was used in study [46] to enhance dataset richness.

### h: PADDING

Adding padding or empty pixels around an image, as demonstrated in study [39], can also be an effective augmentation method to enrich the dataset.
i: OTHER METHODS

Additional augmentation techniques found in the literature include:

* mirroring [35]: Creating mirrored versions of images.

* shifting [31]: Slightly moving the image within the frame.

* cropping [47]: Extracting parts of the image to create variations.

* blurring [24]: Applying blur effects to images.

* zooming [31]: Adjusting the zoom level of images.

* distortion [38]: Introducing distortions to simulate various conditions.

F. METRIC (MODEL PERFORMANCE EVALUATION)

After a deep learning model processes data, it is crucial to evaluate its performance to assess its quality and effectiveness [108]. This evaluation helps researchers understand the model's strengths and weaknesses and is known as metric evaluation. Each metric serves a distinct purpose, making it essential for researchers to select appropriate metrics for accurate interpretation of model performance. Generally, these metrics are based on four basic components:

* True Positives (TP): Refers to the number of correctly identified objects by the model.

* True Negatives (TN): Refers to the number of instances where the model correctly identified the absence of objects.

* False Positives (FP): Refers to the number of erroneous detections of non-existent objects by the model.

* False Negatives (FN): Refers to the number of actual objects that the model failed to detect.

From the reviewed papers, we compiled a list of commonly used metrics for detection, classification, and segmentation tasks, as summarized in Table 4. The metrics considered here include accuracy, which calculates the percentage of correct predictions by dividing the combined total of true positives and true negatives by the overall number of predictions made. The total number of predictions is the sum of true positives, true negatives, false positives, and false negatives [42]. Precision measures the proportion of correct positive predictions out of the total positive predictions made by the model [24]. Specificity assesses how well the model identifies negative cases without producing significant errors [34]. Negative predictive value indicates how often a negative prediction is correct [31], [34]. Precision and recall can be combined into a single value using the F1-score and F2-score, where the F1-score gives equal weight to precision and recall, while the F2-score prioritizes recall [24]. The area under the curve (AUC) shows the model's ability to distinguish between positive and negative classes [51]. Mean average precision (mAP) measures the average accuracy of the model in detecting objects across different classes [24]. In image segmentation, the Dice coefficient is frequently used to quantify the similarity between two samples [21]. The Matthew Correlation Coefficient (MCC) measures the

<table><caption>TABLE 4. List of metrics obtained from the collected papers.</caption><thead><tr><th>Metric</th><th>Formula</th></tr></thead><tbody><tr><td>Accuracy [27], [32], [34], [42]</td><td>Acc = <sup>TP + TN</sup>&frasl;<sub>TP + TN + FP + FN</sub></td></tr><tr><td>Precision (Positive Predictive Value) [21], [24], [27], [34], [42]</td><td>Prec = PPV = <sup>TP</sup>&frasl;<sub>TP + FP</sub></td></tr><tr><td>Recall [21], [24], [27], [34], [42]</td><td>Rec = <sup>TP</sup>&frasl;<sub>TP + FN</sub></td></tr><tr><td>Specificity [32], [34]</td><td>Spec = <sup>TN</sup>&frasl;<sub>TN + FP</sub></td></tr><tr><td>Negative Predictive Value [31], [34]</td><td>NPV = <sup>TN</sup>&frasl;<sub>TN + FN</sub></td></tr><tr><td>F1-score [24], [42]</td><td>F1 - score = <sup>2 · Prec · Rec</sup>&frasl;<sub>Prec + Rec</sub></td></tr><tr><td>F2-score [24]</td><td>F2 - score = <sup>5 · Prec · Rec</sup>&frasl;<sub>4Prec + Rec</sub></td></tr><tr><td>Area Under the Curve[51]</td><td>AUC = 1 - <sup>FPR - FNR</sup>&frasl;<sub>2</sub></td></tr><tr><td>Mean Average Precision [24]</td><td>mAP = <sup>1</sup>&frasl;<sub>N</sub> &sum;<sub>i=1</sub><sup>N</sup> AP<sub>i</sub></td></tr><tr><td>Dice [21], [34]</td><td>Dice = <sup>2 · IOU</sup>&frasl;<sub>1 + IOU</sub> = <sup>2 · TP</sup>&frasl;<sub>2 · TP + FP + FN</sub></td></tr><tr><td>Matthew Correlation Coefficient [42]</td><td>MCC = <sup>TP · TN - FP · FN</sup>&frasl;<sub>&radic;(TP + FP)(TP + FN)(TN + FP)(TN + FN)</sub></td></tr><tr><td>Intersection over Union [34]</td><td>IOU = <sup>TP</sup>&frasl;<sub>TP + FP</sub></td></tr><tr><td>F-Measure [27]</td><td>F - Measure = <sup>2 · TP</sup>&frasl;<sub>2 · TP + FP + FN</sub></td></tr><tr><td>Youden Index [49]</td><td>J = Sensitivity + Specificity - 1</td></tr><tr><td>Processing Time Per Image [27]</td><td>PTPI = <sup>Total Processing Time</sup>&frasl;<sub>Number of Images</sub></td></tr></tbody></table>

correlation between predictions and actual classes, particularly when dealing with unbalanced data [24]. Intersection over Union (IoU) evaluates the overlap between predicted and actual objects, which is an important metric in object detection [34]. The Youden Index is used to assess the diagnostic ability of the model, particularly for determining the optimal cut-off point between sensitivity and specificity [49]. Lastly, processing time per image measures the model's time efficiency in processing each image, which is a critical factor in real-time applications [27].

G. META-ANALYSIS

In this section, we conducted a meta-analysis to evaluate and compare the performance of various models tested on different datasets for lesion detection. Although the original data table presents a comprehensive set of performance metrics, the graphs we created display the averages of four key metrics: Accuracy (Acc), Precision (Prec), Recall (Rec), and Specificity (Spec) across different categories and stages. These metrics were selected for their relevance in evaluating model performance for lesion detection and their widespread use in related literature.
Given the complexity of the data, which includes more
than 15 performance metrics and over 30 types of lesions,
comparing performance on a per-lesion and per-metric basis
was deemed impractical. Such an approach would result in an
overly complex and difficult-to-interpret analysis. To address
this, we focused on four key metrics: Accuracy, Precision,
Recall, and Specificity as they are widely recognized as fun-
damental indicators of model performance in lesion detection
tasks. Additionally, the data were grouped by anatomical
category (Tract) and phase (Stage) to provide a clearer and
more manageable overview of performance trends. Fig. 5 and
Fig. 6 focus on comparing average metrics per stage and per
anatomical tract. Fig. 5 compares Stage 1 and Stage 2, reveal-
ing that Stage 1 generally outperforms Stage 2 in Precision
(85.72%), Accuracy (93.06%), and Specificity (92.66%).
Conversely, Stage 2 demonstrates a slightly higher Recall
(92.64%), reflecting its strength in identifying all relevant
cases. These differences highlight how each stage priori-
tizes specific aspects of performance. Fig. 6 complements
this analysis by showing the distribution of performance
across anatomical tracts. Among the tracts, the Respiratory
tract achieves the highest overall performance, particularly
in Accuracy (98.12%) and Precision (97.70%), while the
Reproductive tract shows less optimal results due to miss-
ing Precision and Specificity data. The Gastrointestinal and
Urinary tracts exhibit relatively balanced performance but are
also affected by data gaps in some metrics.

<figure><img src="image_6.png" /><figcaption>FIGURE 5. Average performance metrics by stage.</figcaption></figure>

<figure><img src="image_7.png" /><figcaption>FIGURE 6. Average performance metrics by tract.</figcaption></figure>

Building on this, Fig. 7 explores the average metrics
across datasets (Private, Public, and Both) for four tract

categories. The dataset Both demonstrates superior perfor-
mance in Accuracy for the Gastrointestinal tract (94.56%)
and Precision for the Respiratory tract (97.70%). The Pri-
vate dataset also achieves high Accuracy in the Respiratory
(98.12%) and Gastrointestinal (92.11%) tracts, while the
Public dataset shows moderate Accuracy for the Gastroin-
testinal tract (90.01%). However, several metrics are missing,
particularly for Precision and Specificity in the Public and
Both datasets, limiting a comprehensive evaluation. Fig. 8
further investigates the comparison of metrics by datasets
across two stages. The dataset Both consistently performs
best, achieving the highest Accuracy (94.56%) and Precision
(97.90%) in Stage 1, as well as strong Recall (96.90%) in
Stage 2. In contrast, data availability for the Public dataset
is sparse, particularly for Stage 2, which hinders meaningful
cross-stage comparisons.

It is important to note that these findings are limited by
the lack of data for certain metrics, particularly in the Repro-
ductive and Urinary tracts, as well as for specific datasets
and stages. This limitation stems from the initial focus of the
systematic literature review (SLR), which primarily aimed to
identify and document models used for lesion detection rather
than to directly compare their performances. The diversity in
lesion types, datasets, and evaluation methodologies across
studies further complicates direct comparisons, as variations
in metrics and reporting practices make such analyses impr prac-
tical and potentially misleading.

To address this, we adopted a meta-analytical approach
based on average metrics—Accuracy, Precision, Recall, and
Specificity—that are commonly reported in the literature.
By focusing on these widely used metrics and grouping
data by anatomical tracts and phases, the analysis provides a
clearer overview of performance trends while simplifying the
complexity inherent in comparing studies with heterogeneous
data. This approach aligns with the study’s intent to offer
insights into model performance trends rather than definitive
comparisons. It ensures the findings remain interpretable and
practically relevant, even within the constraints posed by the
available data. Future research should prioritize harmonized
reporting standards and larger datasets to enable more robust,
detailed comparisons across diverse lesion types and clinical
contexts.

IV. DISCUSSION

The findings of this systematic literature review address
critical questions related to lesion detection using deep learn-
ing, as outlined in the research questions. These include the
types of lesions detected, the methods and datasets used, the
role of augmentation techniques, and the evaluation metrics
applied. While these topics were comprehensively explored
in the Results section, this discussion extends beyond the
initial scope of the research questions to address broader
challenges and considerations raised throughout the study
process. These include the ethical and privacy concerns sur-
rounding medical data, biases in datasets, emerging trends
in lesion detection methods, and practical challenges in
<figure><img src="image_8.png" /><figcaption>FIGURE 7. Comparison of average metrics across datasets and tracts.</figcaption></figure>

<figure><img src="image_9.png" /><figcaption>FIGURE 8. Comparison of average metrics across datasets and stage.</figcaption></figure>

real-time deployment within clinical workflows. By bridging the insights derived from the research questions with these broader considerations, this discussion aims to provide a holistic understanding of the opportunities and challenges in advancing deep learning-based lesion detection toward clinical implementation.

## A. BIAS IN DATASETS

Bias in datasets represents a significant challenge in developing deep learning-based lesion detection models. Various forms of bias, including category imbalance, biases stemming from dataset sources, inconsistencies in annotation

standards, and methodological limitations, have been identified as critical factors impacting model performance. These issues highlight the importance of addressing dataset-related biases to ensure the robustness and generalizability of lesion detection systems.

### 1) CATEGORY IMBALANCE IN DATASETS

Category imbalance is a recurring issue that can affect a model's ability to accurately detect minority classes. For instance, the SSPH_WL dataset in [44] highlights an uneven distribution, where adenomas (1048) are significantly more prevalent compared to polyps (381) and cancers (280). This
study mitigated the imbalance by employing LSDA-Loss, which prioritizes minority categories using prior probabilities, demonstrating an effective strategy for addressing this challenge. Similarly, the KID dataset in [26] contains fewer samples for polypoid categories. This was addressed through data augmentation techniques such as rotation, flipping, and color-based methods, which enriched the representation of minority classes.

In [31], the gastric lesion detection dataset exhibited a high proportion of negative images (192,290) compared to positive ones (9,075), which impacted the Positive Predictive Value (PPV) and increased false-positive rates. Additionally, the limited availability of gastric cancer images for training due to patient constraints underscores the need for multicenter collaboration to diversify datasets. [22] also encountered class imbalance, where ulcer samples were underrepresented compared to adenomas and cancers. Through geometric transformations, the study successfully augmented ulcer samples to improve their representation. In [29], a small number of gastric cancer images necessitated the use of a five-fold cross-validation strategy, ensuring balanced representation of minority classes. [45] highlighted the limited number of images for coarse and precision annotations, which may influence the model's sensitivity toward cancer detection.

## 2) DATASET SOURCE BIAS

Using datasets from single sources can introduce demographic and environmental biases, which may limit the generalizability of models to diverse populations. For example, the dataset from Lingang Hospital in [25] reflects local demographics and clinical conditions. While this focus supports the development of context-specific models, external validation is crucial for broader applicability. Similarly, [43] utilized a small dataset of 352 endoscopic video images, which highlights the importance of additional data collection to enhance clinical generalization. In [23], the limited number of endoscopists involved in data collection suggests opportunities to expand representation and ensure diverse clinical perspectives.

## 3) ANNOTATION AND DATASET STANDARD BIAS

Annotation inconsistencies and varying data collection standards can also influence model reliability. [41] addresses the “island effect,” where single-center datasets may limit generalizability. This study demonstrated the benefits of incorporating multi-source data, including public datasets like CVC-ClinicDB, Kvasir-SEG, and ETIS-Larib, which enhanced model robustness. In [35], the reliance on static images and the absence of grading scales for evaluating image quality highlighted the need for standardized annotation protocols. Expanding validation to include full-length videos could further strengthen the clinical applicability of the model.

[40] acknowledged data imbalances before augmentation and suggested that deeper discussions on annotation standards could provide additional insights. [29] addressed

potential annotation bias by masking alphanumeric characters in initial images, ensuring that predictions were not influenced by extraneous information. Meanwhile, [23] highlighted how the use of non-magnified static images limits the dataset's ability to represent dynamic clinical scenarios effectively.

## 4) PUBLIC DATASET LIMITATIONS

The accessibility and scope of public datasets are essential for developing inclusive models. [26], [34] noted that datasets like KID and Hyper-Kvasir, although valuable, have limited category coverage. For instance, [34] described a dataset comprising 1,003 images from 319 patients, with imbalanced class distributions that could affect generalizability. Expanding these datasets to include diverse lesion types and broader patient demographics remains a critical area for future research.

## 5) METHODOLOGICAL BIAS

Methodological approaches, such as quantization and optimization, may introduce unintended biases that impact model performance. [24] discussed the implications of quantization, where weight and activation distributions can be altered, particularly when asymmetrical values are involved. Advanced techniques like optimal clipping ranges and adaptive quantization were used to mitigate accuracy degradation.

In [35], validation on static images, rather than full-length videos, was identified as a potential methodological limitation. Expanding validation to dynamic datasets could improve real-world applicability. Similarly, [23] identified imbalances in lesion counts and viewing angles across different imaging modes, which could influence comparative evaluations. [31] emphasized the importance of transitioning from static images to real-time video datasets to better align with clinical practices.

# B. CHALLENGE IN CLINICAL IMPLEMENTATION

The implementation of deep learning models for lesion detection in clinical settings faces several challenges. These challenges primarily include regulatory barriers, interpretability issues, and hardware limitations.

## 1) REGULATORY BARRIERS

Most studies reviewed do not explicitly discuss regulatory challenges. However, some highlight the importance of cross-center validation to ensure compliance with international standards. For example, datasets collected from single centers, such as Lingang Hospital, may reflect local demographics and clinical conditions, necessitating external validation to extend the applicability of models to more diverse populations [25]. Such validation is critical to adhere to regulations across various regions.

## 2) INTERPRETABILITY ISSUES

Interpretability is a critical factor in the clinical adoption of deep learning models, as it directly influences the level
of trust clinicians place in model outputs. To address this, Explainable Artificial Intelligence (XAI) techniques have gained significant attention, offering methods to make deep learning models more transparent and comprehensible.

For instance, Grad-CAM (Gradient-weighted Class Activation Mapping) and its variant Guided-GradCAM have been utilized in gastrointestinal lesion detection using capsule endoscopic images [36]. These techniques generate heatmaps that highlight the most relevant regions in the image, providing a visual explanation of how the CNN-based GINet model identifies lesions. By visualizing the decision-making process, clinicians gain deeper insights into the model's outputs, fostering greater confidence in its use.

Similarly, Grad-CAM has been employed in the detection of ulcerative colitis lesions using the UCFN-Net model [42]. Heatmaps generated by Grad-CAM revealed critical image regions influencing the model's predictions, allowing clinicians to assess the reliability and focus of the model. This approach not only enhances transparency but also validates the model's effectiveness in handling complex medical imaging tasks.

Another study applied Grad-CAM in real-time detection of nasopharyngeal carcinoma during nasopharyngeal endoscopy [47]. The technique produced activation maps that highlighted specific morphological features of lesions, such as elevated areas or complex vascular structures, which are critical for diagnosis. This capability not only supports diagnostic decision-making but also aids in identifying optimal biopsy locations, further bridging the gap between automated systems and clinical practice.

The integration of XAI techniques like Grad-CAM into deep learning models represents a significant step forward in improving their interpretability. By providing visual explanations of model outputs, these methods enhance transparency, build clinician trust, and pave the way for broader adoption of AI-driven tools in clinical workflows.

### 3) HARDWARE LIMITATIONS

Computational capacity is often a bottleneck in deploying deep learning models in clinical environments. Several studies highlight the importance of optimization techniques, such as quantization and real-time validation, to address these limitations. For example, INT8 and FP16 quantization has been shown to improve inference efficiency on low-power hardware, such as capsule endoscopy devices [24]. Additionally, model optimization for real-time detection, as demonstrated in ureteral orifice detection systems, ensures that resource-constrained hardware can effectively support clinical applications [50].

## C. EMERGING TRENDS IN LESION DETECTION

The rapid evolution of deep learning methods in medical imaging has opened avenues for innovative approaches to lesion detection. These advancements aim not only to enhance the accuracy and efficiency of existing models but also to address key challenges such as dataset limitations,

hardware constraints, and clinical applicability. This section highlights emerging trends in lesion detection, emphasizing their potential to bridge gaps identified in regulatory compliance, real-time deployment, and ethical concerns.

### 1) FEDERATED LEARNING FOR PRIVACY-PRESERVING COLLABORATION

Federated learning (FL) has emerged as a transformative approach to address privacy concerns while enabling collaborative model training across multiple institutions. Unlike traditional centralized learning, which requires direct data aggregation, FL trains models locally on distributed datasets and aggregates knowledge through a central server without sharing raw data. This paradigm ensures compliance with privacy regulations while fostering the development of robust and generalized models.

A notable application of FL is demonstrated in wireless capsule endoscopy (WCE), a diagnostic tool for small bowel pathology. In a recent study, FL was employed to analyze diverse pathological manifestations in WCE video frames collected from multiple centers. This decentralized approach achieved a 10–12% performance improvement on heterogeneous test sets compared to centralized models. The study highlighted the ability of FL to generalize across diverse datasets while preserving privacy, addressing a critical bottleneck in the adoption of AI-based diagnoses in clinical settings [109].

Similarly, federated frameworks have shown promise in multi-center analysis for endoscopic ultrasound (EUS)-based gastrointestinal stromal tumor (GIST) diagnosis. The introduction of the Unified Federated Learning Framework (UFLF) alongside a Data Pyramid Structure (DPS) demonstrated significant advancements in handling data sparsity and missing labels. The approach achieved high diagnostic accuracy (0.984) and AUC (0.927) across five centers, outperforming state-of-the-art methods. The framework not only preserved data privacy but also optimized multi-task learning by leveraging segmentation and aggregation strategies, enhancing both performance and interpretability [110].

Federated learning thus represents a key trend in advancing lesion detection models. By enabling collaborative learning across institutions while addressing privacy concerns, FL frameworks facilitate the development of robust, generalized models suitable for global deployment. Future research should focus on further optimizing FL algorithms and addressing challenges such as communication efficiency and model heterogeneity to maximize their potential in clinical practice.

### 2) LIGHTWEIGHT MODELS FOR RESOURCE-CONSTRAINED ENVIRONMENTS

Deploying deep learning models in real-time clinical workflows requires lightweight architectures that address hardware limitations typical of resource-constrained environments, such as mobile diagnostic tools and low-power
medical imaging systems. Recent advancements, such as an attention-based classification model for gastric lesions [111] and the LDCNet for laparoscopic image segmentation [112], demonstrate how lightweight designs can maintain high accuracy while minimizing computational demands. The attention-based model achieves this by incorporating feature fusion and selective emphasis on critical regions, optimizing for efficiency without sacrificing performance. Similarly, LDCNet uses dynamic convolution and receptive field blocks to improve segmentation accuracy in laparoscopic procedures, ensuring real-time usability on low-power devices.

These lightweight models are pivotal for extending deep learning applications to resource-constrained settings. By enabling deployment on portable endoscopic and laparoscopic systems, they democratize access to advanced imaging tools in environments with limited computational resources. Such approaches balance accuracy and efficiency, paving the way for real-time deep learning adoption in clinical workflows.

### 3) MULTIMODAL APPROACHES FOR ENHANCED DETECTION

The integration of multimodal data has emerged as a transformative approach in lesion detection, offering the potential to enhance diagnostic accuracy and robustness by leveraging diverse sources of information. Multimodal frameworks enable the synthesis of complementary data types, such as endoscopic images, pathological slides, and clinical records, to provide a more comprehensive understanding of complex medical conditions. Recent advancements have demonstrated significant potential in addressing both disease-specific challenges and real-time clinical applications.

For disease-specific multimodal frameworks, RFIA-Net combines convolutional neural networks (CNNs) with Transformer architectures to classify stage I oesophageal cancer images. This hybrid approach utilizes CNNs for localized feature extraction while leveraging Transformers for global semantic understanding, achieving remarkable results such as an AUC of 0.9973 and an accuracy of 0.9902 in oesophageal cancer staging tasks. By integrating spatial and semantic information, RFIA-Net exemplifies how multimodal architectures can optimize diagnostic precision in specialized conditions like early-stage oesophageal cancer [113]. Similarly, MICDnet focuses on the diagnosis of Crohn's disease by incorporating data from colonoscopy images, pathology slides, and clinical texts. This multimodal approach allows for a holistic analysis of inflammatory disease presentations, enhancing diagnostic accuracy by integrating complementary modalities [114]. Another multimodal framework for Barrett's esophagus combines low-level and high-level modalities, simulating the collaborative data processing seen in human cognition. This framework significantly improves the prediction and risk grading of Barrett's esophagus, aiding early detection and clinical decision-making [115].

In real-time multimodal systems, the Endo-CRC model exemplifies the practical integration of multimodal data for

colorectal cancer diagnosis. Combining white light (WL) and image-enhanced endoscopy (IEE) images, Endo-CRC is capable of accurately assessing colorectal cancer invasion depth. Validated on video datasets, this system achieved an impressive 100.00% accuracy in real-time video-based evaluations, demonstrating its readiness for live clinical workflows. This model underscores the growing potential of real-time multimodal systems in supporting endoscopists during procedures, bridging the gap between experimental frameworks and clinical implementation [116].

In addition to these frameworks, several other innovative approaches have been developed to enhance lesion detection and diagnostic accuracy across diverse medical contexts. A multimodal ensemble framework for endoscopic ultrasound (EUS) analysis enables morphological and functional evaluation of the pancreas, aiding in the detection of mild pancreatic abnormalities. This approach combines multiple data sources to support the clinical suspicion of early chronic pancreatitis, providing valuable diagnostic insights [110]. Furthermore, the E-DU multimodal segmentation method focuses on semantic gap compensation for effective segmentation across multiple medical imaging modalities. It achieves robust performance and operational efficiency, underscoring its potential for broad clinical application [117].

These advancements in multimodal learning highlight the versatility of integrating diverse data modalities in lesion detection. By combining localized, semantic, and temporal features, these systems offer robust solutions for both disease-specific and real-time applications, paving the way for more precise and actionable clinical outcomes.

### 4) FEW-SHOT LEARNING

The reliance on large annotated datasets remains a significant barrier in developing robust lesion detection models. Few-shot learning (FSL) techniques offer promising solutions by enabling models to adapt to new tasks with minimal training examples. These approaches are particularly valuable for underrepresented lesion types and rare conditions, ensuring more equitable diagnostic outcomes.

For instance, a few-shot learning (FSL) system was developed to differentiate between intestinal tuberculosis (ITB) and Crohn's disease (CD), two conditions that are notoriously challenging to distinguish using conventional deep learning methods due to limited data availability. This system utilized a 2-way, 3-shot FSL model that integrated dual transfer learning and metric learning strategies, built on the Xception architecture. By leveraging dual transfer learning with additional training from oesophagitis images sourced from the HyperKvasir dataset, the FSL model achieved an area under the curve (AUC) of 0.81, significantly outperforming both single transfer learning models (AUC 0.56) and even seasoned specialists (AUC 0.61) in distinguishing CD from ITB. This demonstrates the potential of FSL in enhancing diagnostic capabilities, particularly for rare or underrepresented conditions [118].
Moreover, a recent study applied a similar FSL approach to differentiate between gastric signet ring cell carcinoma (SRCC), adenocarcinoma, and benign ulcers, which are challenging to diagnose due to data scarcity and tumor heterogeneity. The model, based on EfficientNetV2-S, utilized dual pretraining—first on ImageNet, and then on esophageal endoscopic images. This framework achieved impressive results, with an accuracy of 79.4%, macro-AUC of 0.763, and sensitivity of 68.8%, outperforming both traditional deep learning models and senior endoscopists. These findings further underscore the promise of FSL in computer-aided diagnosis, especially for rare and underrepresented medical conditions [119]. These methods hold promise for broadening the scope of automated diagnostic systems to address rare lesions and conditions that are often overlooked due to data scarcity.

## D. REAL-TIME DEPLOYMENT IN CLINICAL PRACTICE

Real-time deployment of deep learning-based lesion detection systems presents a critical frontier in the integration of artificial intelligence (AI) into clinical endoscopy and laparoscopy. This capability is essential for achieving immediate, actionable insights during procedures, thereby enhancing clinical decision-making and improving patient outcomes. However, the successful implementation of real-time systems hinges on overcoming specific computational and operational challenges.

Several studies in this review demonstrate progress toward real-time deployment. [21] highlights the implementation of the YOLO-V8 network for capsule endoscopy, where computational efficiency is critical due to hardware constraints. The study specifically focuses on optimizing model performance to enable real-time inference on resource-limited devices. Similarly, [26] introduces CSS-YOLOv7, an optimized version of YOLOv7, which enhances detection speed and precision, laying the groundwork for potential real-time applications in clinical settings.

In the context of colonoscopy, [39] achieves real-time performance using a lightweight YOLOv3 MSF model optimized for colonoscopy systems. The model demonstrates the ability to process high-resolution images efficiently, which is essential for supporting endoscopists during live procedures. [50] further illustrates real-time applicability by optimizing a YOLO-based model for urinary endoscopy. This study specifically addresses the computational limitations of endoscopic devices and successfully demonstrates real-time inference while maintaining high accuracy. Lastly, [53] adopts hybrid methods to enhance processing speed, showing promise for real-time applications in detecting lesions across diverse body systems.

Additional studies provide insights into the potential for real-time deployment, although most rely on simulation or controlled experimental conditions rather than live clinical workflows. [24] demonstrates the use of YOLOv4 with INT8 quantization, achieving an inference rate of 172 frames per second (FPS).

This speed is indicative of the model's capability for real-time processing in resource-constrained environments. However, the study does not validate its performance in live clinical settings, leaving its real-world applicability to be confirmed. [40] explores detection of gastrointestinal tract disorders using deep learning methods applied to video datasets, including Hyper Kvasir. Testing on video data represents a step closer to clinical application by mimicking real-world scenarios. Nevertheless, the study does not include detailed latency metrics or live testing, limiting its validation for clinical workflows. [43] employs a distribution-aware loss function to optimize detection of rare lesion categories, claiming suitability for real-time applications. While promising, the model was evaluated on a small dataset of static images, necessitating further validation in dynamic, video-based, or clinical environments.

Despite these advancements, challenges persist. The computational demands of deep learning models often necessitate specialized hardware, such as GPUs, which may not be readily available in all clinical settings. Furthermore, the trade-off between speed and accuracy requires careful optimization to ensure that real-time models do not compromise diagnostic reliability. Key obstacles include the need for robust testing on video data, integration of models into existing endoscopic hardware, and addressing variability in real-world imaging conditions, such as noise or lighting inconsistencies. Additionally, improving interpretability and ensuring regulatory compliance are critical for broader acceptance and usability in clinical practice.

Future research should prioritize large-scale, real-world validations of these models in live endoscopic settings. Enhancing the interpretability, adaptability, and regulatory approval processes will help bridge the gap between experimental success and practical implementation, ensuring these systems deliver measurable improvements in patient care.

## E. ETHICAL AND PRIVACY CONCERNS

The integration of artificial intelligence (AI) in medical diagnostics involves managing sensitive patient data, raising significant ethical and privacy concerns. Addressing these challenges is vital to ensure the safe and equitable deployment of AI models in clinical settings. While the reviewed studies primarily emphasize technical advancements, they provide limited explicit discussions on the broader implications of utilizing medical data in AI systems. This section consolidates the ethical and privacy considerations derived from the reviewed literature.

Digital transformation in healthcare has ushered in numerous innovations, including teleconsultations, enhanced patient data management, and AI-based diagnostic tools. These advancements have improved accessibility and efficiency in healthcare services but have simultaneously amplified privacy risks. For example, data collection in Korea adhered to national privacy regulations but lacked detailed technical measures like encryption or advanced anonymization to enhance data security [22]. Similarly, studies from
Denmark ensured compliance with local data protection laws but highlighted challenges in aligning with broader international frameworks [35]. The increasing reliance on large datasets to train AI systems necessitates robust privacy safeguards to prevent unauthorized access and misuse of sensitive information.

## 1) ETHICAL CONSIDERATIONS IN AI FOR MEDICAL DIAGNOSTICS

Ethical issues in the deployment of AI systems include data ownership, consent, fairness, and accountability. Transparent consent processes are pivotal, as exemplified by studies that secured ethics committee approvals and informed consent from patients [23], [33], [35]. Retrospective studies employing opt-out mechanisms [38], [49] have been compliant with ethical norms, but these approaches require careful implementation to avoid ambiguity in patient participation.

Bias in datasets presents another ethical challenge, potentially leading to unequal diagnostic performance. For instance, colorectal datasets with significant category imbalances may result in biased AI models, affecting certain patient groups disproportionately [40], [44]. Similarly, the use of single-center datasets, as noted in [42], raises concerns about the generalizability of these systems to diverse populations. Developing inclusive datasets and implementing bias mitigation strategies are critical for ensuring equitable healthcare outcomes.

While technical biases in datasets can undermine model accuracy and fairness (discussed in Section A), ethical implications of such biases extend to potential disparities in healthcare access and outcomes, which necessitate broader accountability.

## 2) PRIVACY CONCERNS AND SAFEGUARDS

Safeguarding patient information is paramount in AI applications. Anonymization and adherence to ethical standards are foundational practices cited across studies. [23], [37], [48] emphasize anonymization protocols as a critical step in protecting patient identities. However, anonymized data can still be vulnerable to re-identification through advanced computational techniques, necessitating additional measures like differential privacy and data encryption.

Compliance with ethical guidelines, such as the Declaration of Helsinki, further reinforces responsible use of medical data. [42], [47], [51] highlight adherence to institutional and international ethical standards, underscoring the importance of ethical oversight in medical research. Yet, the lack of discussions on advanced privacy-preserving technologies, such as federated learning, remains a gap in many studies.

## 3) BROADER IMPLICATIONS AND CONSEQUENCES

Privacy breaches in medical data can have severe consequences, including workplace discrimination, increased insurance premiums, and erosion of trust in healthcare systems. Psychological distress among affected individuals further compounds the damage. [35], [49] emphasize the

critical need for stringent data protection to prevent such outcomes. Moreover, regulatory challenges in aligning diverse privacy laws, such as GDPR in the EU and HIPAA in the US, hinder the development of global datasets necessary for reducing AI biases.

## 4) PATHWAYS FORWARD

To address these concerns, studies must adopt comprehensive privacy-preserving frameworks and ethical practices. Federated learning, which allows model training on decentralized datasets without transferring raw data, offers a promising solution to maintain data sovereignty while minimizing exposure risks. [45], [52] advocate for hybrid privacy-preserving approaches that combine multiple methods, enhancing security and compliance.

Additionally, fostering regulatory harmonization across regions and advancing global collaborations in dataset development will be crucial. Incorporating bias audits, accountability mechanisms, and interpretability features into AI systems can further align technology with ethical norms and clinical needs.

# V. CONCLUSION

This systematic literature review investigated the application of deep learning in lesion detection using endoscopic and laparoscopic modalities, addressing six research questions (RQ1-RQ6). Gastrointestinal lesions were found to dominate research focus, with fewer studies targeting lesions in the respiratory, urinary, and reproductive tracts. This highlights the need for a more diverse representation of datasets to ensure broad applicability and relevance in clinical practice.

The review showcased the significant potential of deep learning models for lesion detection, with one-stage methods excelling in real-time performance and two-stage approaches offering higher accuracy and segmentation quality. Variations and enhancements, including attention mechanisms and pretrained weights, were identified as critical factors in improving model effectiveness, demonstrating advancements in feature extraction and generalization. Datasets play a pivotal role in model development, with private datasets being more prevalent than public ones. Preprocessing and augmentation techniques, such as resizing, noise reduction, and data transformations, were instrumental in enhancing dataset quality and model performance, ensuring better generalizability. The meta-analysis further provided insights into trends across anatomical tracts, stages, and datasets, revealing strengths and limitations in the models' performance metrics, including accuracy, precision, recall, and specificity. The focus on average metrics and grouping by tract and phase facilitated a manageable analysis while highlighting key performance patterns.

While promising, the findings underscore several challenges, including dataset biases, computational demands, and limited generalizability, particularly in resource-constrained settings. Ethical and privacy considerations remain critical to equitable AI deployment. Promising directions, such
as federated learning, multimodal approaches, and optimized lightweight models, offer opportunities for addressing these challenges and improving scalability and adaptability. By integrating these advancements, this work lays a robust foundation for the development of scalable, accurate, and ethical lesion detection systems, contributing to both research innovation and clinical excellence.

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<figure><img src="image_10.png" /></figure>

**NAIM ROCHMAWATI** received the master's degree in computer science from the Institut Teknologi Bandung, in 2010. She is currently pursuing the Ph.D. degree with the Institut Teknologi Sepuluh Nopember. She is currently an Assistant Professor in informatic engineering with Universitas Negeri Surabaya. She has published over 30 international publications and conference papers. Her research interests include computer vision, deep learning, and machine learning.

<figure><img src="image_11.png" /></figure>

**CHASTINE FATICHAH** (Member, IEEE) received the Ph.D. degree from Tokyo Institute of Technology, Japan, in 2012. She is currently a Professor with the Institut Teknologi Sepuluh Nopember, Surabaya. She has published more than 351 international publications and conference papers. Her research interests include artificial intelligence, image processing, and data mining.

<figure><img src="image_12.png" /></figure>

**BILQIS AMALIAH** (Member, IEEE) received the Ph.D. degree from the Institut Teknologi Sepuluh Nopember (ITS), Surabaya, in 2020. She is currently an Assistant Professor with ITS. With over 30 international publications and conference papers, her research interest includes optimization.

<figure><img src="image_13.png" /></figure>

**AGUS BUDI RAHARJO** (Member, IEEE) received the Ph.D. degree from Aix-Marseille University, France, in 2020. He is currently an Assistant Professor with the Institut Teknologi Sepuluh Nopember, Surabaya. He has published over 35 international publications and conference papers. His research interests include machine learning, cloud computing, telemedicine, and data science.

<figure><img src="image_14.png" /></figure>

**FRÉDÉRIC DUMONT** is currently a Medical Doctor with the Institut de Cancérologie de l'Ouest—Centre René Gauducheau. With extensive skills and expertise in laparoscopic surgery, surgical oncology, surgical anatomy, colorectal surgery, hepatobiliary surgery, colorectal cancer, gastric cancer, abdominal surgery, surgery, laparoscopy, surgical procedures, oncology, general surgery, liver, and tumors. He has contributed to more than 190 research publications.

<figure><img src="image_15.png" /></figure>

**EMILIE THIBAODEAU** is currently a Medical Doctor specializing in oncology with the Institut de Cancérologie de l'Ouest—Centre René Gauducheau. With a focus on cancer, she has developed expertise in various aspects of oncological care. She has contributed to over 51 research publications, furthering the understanding and treatment of cancer.

<figure><img src="image_16.png" /></figure>

**CÉDRIC DUMAS** is currently an Associate Professor with IMT Atlantique, specializing in human-computer interaction. With a focus on medical simulation and training, he has become an expert in leveraging technology to advance medical education. He has contributed to over 74 research publications, particularly in the field of medical simulation and training, enhancing the knowledge and practice of medical professionals worldwide.