ADKU/ResearchGPT_model_dataset huggingface dataset:
Estimating infant age from skull X-ray images using deep learning
Authors: Heui Seung Lee, Jaewoong Kang, So Eui Kim, Ji Hee Kim, Bum-Joo Cho

This study constructed deep learning models using plain skull radiograph images to predict the accurate postnatal age of infants under 12 months. Utilizing the results of the trained deep learning models, it aimed to evaluate the feasibility of employing major changes visible in skull X-ray images for assessing postnatal cranial development through gradient-weighted class activation mapping. We developed DenseNet-121 and EfficientNet-v2-M convolutional neural network models to analyze 4933 skull X-ray images collected from 1343 infants. Notably, allowing for a ± 1 month error margin, DenseNet-121 reached a maximum corrected accuracy of 79.4% for anteroposterior (AP) views (average: 78.0 ± 1.5%) and 84.2% for lateral views (average: 81.1 ± 2.9%). EfficientNet-v2-M reached a maximum corrected accuracy 79.1% for AP views (average: 77.0 ± 2.3%) and 87.3% for lateral views (average: 85.1 ± 2.5%). Saliency maps identified critical discriminative areas in skull radiographs, including the coronal, sagittal, and metopic sutures in AP skull X-ray images, and the lambdoid suture and cortical bone density in lateral images, marking them as indicators for evaluating cranial development. These findings highlight the precision of deep learning in estimating infant age through non-invasive methods, offering the progress for clinical diagnostics and developmental assessment tools.
Deep learning based damage detection of concrete structures
Authors: Maheswara Rao Bandi, Laxmi Narayana Pasupuleti, Tanmay Das, Shyamal Guchhait

Damage detection of any civil engineering structure is a part of the interest in the field of engineering from which one can estimate the stability and lifetime of the structure. With the advancement of technology in the field of infrastructure, damage assessment of any structure with the help of convolutional neural networks (CNNs) and deep learning is gaining importance because of the ease with which it can detect the damage. By using these computer-aided techniques, we can reduce manpower and detect damage in inaccessible places that we cannot see directly. In the current study, we used ResNet-50, which is a part of a deep convolutional neural network with 50 layers deep in it. ResNet-50 is a subclass of convolutional neural networks most popularly used for image classification. Furthermore, we used the image data set collected from the Utah State University, Logan, Utah, USA. This data set contains nearly 56,000 annotated images of both cracked and non-cracked bridge deck images, wall images, and pavement images. These images contain a variety of obstructions, such as shadows and surface roughness in some cases. The present study aimed to train and test the images and compare the accuracy of the results among themselves with an increase in training data. We devised an algorithm to measure cracks as soon as we detected them. The results show that the Resnet-50 architecture was in good agreement with the developed algorithm.
A Comparative Analysis of Deep Learning-Based Approaches for Classifying Dental Implants Decision Support System
Authors: Mohammed A. H. Lubbad, Ikbal Leblebicioglu Kurtulus, Dervis Karaboga, Kerem Kilic, Alper Basturk, Bahriye Akay, Ozkan Ufuk Nalbantoglu, Ozden Melis Durmaz Yilmaz, Mustafa Ayata, Serkan Yilmaz, Ishak Pacal

This study aims to provide an effective solution for the autonomous identification of dental implant brands through a deep learning-based computer diagnostic system. It also seeks to ascertain the system’s potential in clinical practices and to offer a strategic framework for improving diagnosis and treatment processes in implantology. This study employed a total of 28 different deep learning models, including 18 convolutional neural network (CNN) models (VGG, ResNet, DenseNet, EfficientNet, RegNet, ConvNeXt) and 10 vision transformer models (Swin and Vision Transformer). The dataset comprises 1258 panoramic radiographs from patients who received implant treatments at Erciyes University Faculty of Dentistry between 2012 and 2023. It is utilized for the training and evaluation process of deep learning models and consists of prototypes from six different implant systems provided by six manufacturers. The deep learning-based dental implant system provided high classification accuracy for different dental implant brands using deep learning models. Furthermore, among all the architectures evaluated, the small model of the ConvNeXt architecture achieved an impressive accuracy rate of 94.2%, demonstrating a high level of classification success.This study emphasizes the effectiveness of deep learning-based systems in achieving high classification accuracy in dental implant types. These findings pave the way for integrating advanced deep learning tools into clinical practice, promising significant improvements in patient care and treatment outcomes.
A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer
Authors: Ding Zhang, BingShu Zheng, LiuWei Xu, YiCong Wu, Chen Shen, ShanLei Bao, ZhongHua Tan, ChunFeng Sun

ObjectivesSynchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM.MethodsA total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC).ResultsThe AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874–0.978), 0.897 (95% CI: 0.801–0.994), 0.885 (95% CI: 0.795–0.975), and 0.889 (95% CI: 0.823–0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model.ConclusionsThe radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans.Critical relevance statementThe onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans.Key Points

Prognosis for patients with CRPM is bleak, and early detection poses challenges.


The synergy between radiomics and deep learning proves advantageous in evaluating CRPM.


The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

Graphical Abstract
Transfer learning-based quantized deep learning models for nail melanoma classification
Authors: Mujahid Hussain, Makhmoor Fiza, Aiman Khalil, Asad Ali Siyal, Fayaz Ali Dharejo, Waheeduddin Hyder, Antonella Guzzo, Moez Krichen, Giancarlo Fortino

Skin cancer, particularly melanoma, has remained a severe issue for many years due to its increasing incidences. The rising mortality rate associated with melanoma demands immediate attention at early stages to facilitate timely diagnosis and effective treatment. Due to the similar visual appearance of malignant tumors and normal cells, the detection and classification of melanoma are considered to be one of the most challenging tasks. Detecting melanoma accurately and promptly is essential to diagnosis and treatment, which can contribute significantly to patient survival. A new dataset, Nailmelonma, is presented in this study in order to train and evaluate various deep learning models applying transfer learning for an indigenous nail melanoma localization dataset. Using the dermoscopic image datasets, seven CNN-based DL architectures (viz., VGG19, ResNet101, ResNet152V2, Xception, InceptionV3, MobileNet, and MobileNetv2) have been trained and tested for the classification of skin lesions for melanoma detection. The trained models have been validated, and key performance parameters (i.e., accuracy, recall, specificity, precision, and F1-score) are systematically evaluated to test the performance of each transfer learning model. The results indicated that the proposed workflow could realize and achieve more than 95% accuracy. In addition, we show how the quantization of such models can enable them for memory-constrained mobile/edge devices. To facilitate an accurate, timely, and faster diagnosis of nail melanoma and to evaluate the early detection of other types of skin cancer, the proposed workflow can be readily applied and robust to the early detection of nail melanoma.
RETRACTED ARTICLE: Multimedia Lu Xun literature online learning based on deep learning
Authors: Wang Hongsheng

As a great Chinese thinker and writer in the twentieth century, Lu Xun and his literary works are widely known. However, as a successful cultural communication activist, editor and publisher, we still need to conduct in-depth research on Lu Xun in many aspects. Therefore, based on deep learning, this paper constructs an online multimedia learning system of Lu Xun literature. This system takes the relationship between classical Lu Xun literature and modern multimedia technology as the research object, and compare the calculation effect of other different types of algorithms and this dhraa algorithm. Through the availability of data, the dhraa algorithm is significantly better than other algorithms in the recommendation accuracy, thus proving its effectiveness. This system is managed by two servers and one system. The two servers are database server and web server, respectively. After testing, the system has good bearing capacity, can make up for the limited processing capacity of the server, and ensure the system has high performance. Its performance characteristics also show that the system achieved the expected performance. This paper systematically combines Lu Xun’s literature with modern multimedia, which can provide online learning services for Lu Xun’s literature lovers, thus helping scholars to expand Lu Xun’s research field and academic vision. This paper designs an effective online learning system of Lu Xun’s literature by combining deep learning, multimedia technology and Lu Xun’s literature.
Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models
Authors: Daryl Jacob Jerrish, Om Nankar, Shilpa Gite, Shruti Patil, Ketan Kotecha, Ganeshsree Selvachandran, Ajith Abraham

Lyme disease diagnosis poses a significant challenge, with blood tests exhibiting an alarming inaccuracy rate of nearly 60% in detecting early-stage infections. As a result, there is an urgent need for improved diagnostic methods that can offer more accurate detection outcomes. To address this pressing issue, our study focuses on harnessing the potential of deep learning approaches, specifically by employing model pipelining through progressive resizing and multiple self-supervised learning models. In this paper, we present a comprehensive exploration of self-supervised learning models, including SimCLR, SwAV, MoCo, and BYOL, tailored to the context of Lyme disease detection using medical imaging. The effectiveness and performance of these models are evaluated using standard metrics such as F1 score, precision, recall, and accuracy. Furthermore, we emphasize the significance of progressive resizing and its implications when dealing with convolutional neural networks (CNNs) for medical image analysis. By leveraging deep learning approaches, progressive resizing, and self-supervised learning models, the challenges associated with Lyme disease detection are effectively addressed in this study. The application of our novel methodology and the execution of a comprehensive evaluation framework contribute invaluable insights, fostering the development of more efficient and accurate diagnostic methods for Lyme disease. It is firmly believed that our research will serve as a catalyst, inspiring interdisciplinary collaborations that accelerate progress at the convergence of medicine, computing, and technology, ultimately benefiting public health.
A systematic review of deep learning based image segmentation to detect polyp
Authors: Mayuri Gupta, Ashish Mishra

Among the world’s most common cancers, colorectal cancer is the third most severe form of cancer. Early polyp detection reduces the risk of colorectal cancer, vital for effective treatment. Artificial intelligence methods such as deep learning have emerged as leading techniques for polyp image segmentation that have gained success in advancing medical image diagnosis. This study aims to provide a review of the most recent research studies that have used deep learning methods and models for polyp segmentation. A comprehensive review of deep learning-based image segmentation models is provided based on existing research studies that are essential for polyp segmentation. Convolutional neural networks, encoder–decoder models, recurrent neural networks, attention-based models, and generative models were the most popular deep learning models which play an essential role in detecting and diagnosing polyp at an early stage. Additionally, this study also aims to provide a detailed classification of prominently used polyp image and video datasets. The evaluation metrics for assessing the effectiveness of different methods, models, and techniques are identified and discussed. A statistical analysis of deep learning models based on polyp datasets and performance metrics is presented, with a discussion of future research trends and limitations.
Deep Learning Models for Skin Cancer Classification Across Diverse Color Spaces: Comprehensive Analysis
Authors: Anisha Paul, Asfak Ali, Sheli Sinha Chaudhuri

Color space plays an important role in various aspects of imaging tasks. However, in deep learning-based computer vision, the RGB color model is predominantly employed. This research analyzes the impact of deep convolutional neural networks on cancer classification across different color spaces. The five most popular deep learning models undergo training and testing in eleven color spaces, revealing that YUV, LAB, and YIQ consistently outperform other color models in most cases. RGB images are frequently converted to alternative color spaces for enhanced representation in specific applications, like object detection and segmentation. This transformation induces alterations in the features of the color image due to variations in pixel intensity information across different color models. In this research, the aforementioned principle is applied to the classification of skin cancer using deep learning networks on images of skin lesions. The results exhibit diverse responses, with some networks achieving higher accuracy in alternative color spaces compared to RGB, while others do not. This study provides insights into the classification performance across RGB, HED, HSV, LAB, RGBCIE, XYZ, YCbCr, YDbDr, YIQ, YPbPr, and YUV color spaces. The research aims to illustrate how deep learning facilitates the analysis of skin cancer images in different color spaces.
Scaffolding cooperation in human groups with deep reinforcement learning
Authors: Kevin R. McKee, Andrea Tacchetti, Michiel A. Bakker, Jan Balaguer, Lucy Campbell-Gillingham, Richard Everett, Matthew Botvinick

Effective approaches to encouraging group cooperation are still an open challenge. Here we apply recent advances in deep learning to structure networks of human participants playing a group cooperation game. We leverage deep reinforcement learning and simulation methods to train a ‘social planner’ capable of making recommendations to create or break connections between group members. The strategy that it develops succeeds at encouraging pro-sociality in networks of human participants (N = 208 participants in 13 groups) playing for real monetary stakes. Under the social planner, groups finished the game with an average cooperation rate of 77.7%, compared with 42.8% in static networks (N = 176 in 11 groups). In contrast to prior strategies that separate defectors from cooperators (tested here with N = 384 in 24 groups), the social planner learns to take a conciliatory approach to defectors, encouraging them to act pro-socially by moving them to small highly cooperative neighbourhoods.
Deep-learning-enabled brain hemodynamic mapping using resting-state fMRI
Authors: Xirui Hou, Pengfei Guo, Puyang Wang, Peiying Liu, Doris D. M. Lin, Hongli Fan, Yang Li, Zhiliang Wei, Zixuan Lin, Dengrong Jiang, Jin Jin, Catherine Kelly, Jay J. Pillai, Judy Huang, Marco C. Pinho, Binu P. Thomas, Babu G. Welch, Denise C. Park, Vishal M. Patel, Argye E. Hillis, Hanzhang Lu

Cerebrovascular disease is a leading cause of death globally. Prevention and early intervention are known to be the most effective forms of its management. Non-invasive imaging methods hold great promises for early stratification, but at present lack the sensitivity for personalized prognosis. Resting-state functional magnetic resonance imaging (rs-fMRI), a powerful tool previously used for mapping neural activity, is available in most hospitals. Here we show that rs-fMRI can be used to map cerebral hemodynamic function and delineate impairment. By exploiting time variations in breathing pattern during rs-fMRI, deep learning enables reproducible mapping of cerebrovascular reactivity (CVR) and bolus arrival time (BAT) of the human brain using resting-state CO2 fluctuations as a natural “contrast media”. The deep-learning network is trained with CVR and BAT maps obtained with a reference method of CO2-inhalation MRI, which includes data from young and older healthy subjects and patients with Moyamoya disease and brain tumors. We demonstrate the performance of deep-learning cerebrovascular mapping in the detection of vascular abnormalities, evaluation of revascularization effects, and vascular alterations in normal aging. In addition, cerebrovascular maps obtained with the proposed method exhibit excellent reproducibility in both healthy volunteers and stroke patients. Deep-learning resting-state vascular imaging has the potential to become a useful tool in clinical cerebrovascular imaging.
An Alternative to Cognitivism: Computational Phenomenology for Deep Learning
Authors: Pierre Beckmann, Guillaume Köstner, Inês Hipólito

We propose a non-representationalist framework for deep learning relying on a novel method computational phenomenology, a dialogue between the first-person perspective (relying on phenomenology) and the mechanisms of computational models. We thereby propose an alternative to the modern cognitivist interpretation of deep learning, according to which artificial neural networks encode representations of external entities. This interpretation mainly relies on neuro-representationalism, a position that combines a strong ontological commitment towards scientific theoretical entities and the idea that the brain operates on symbolic representations of these entities. We proceed as follows: after offering a review of cognitivism and neuro-representationalism in the field of deep learning, we first elaborate a phenomenological critique of these positions; we then sketch out computational phenomenology and distinguish it from existing alternatives; finally we apply this new method to deep learning models trained on specific tasks, in order to formulate a conceptual framework of deep-learning, that allows one to think of artificial neural networks’ mechanisms in terms of lived experience.
Slideflow: deep learning for digital histopathology with real-time whole-slide visualization
Authors: James M. Dolezal, Sara Kochanny, Emma Dyer, Siddhi Ramesh, Andrew Srisuwananukorn, Matteo Sacco, Frederick M. Howard, Anran Li, Prajval Mohan, Alexander T. Pearson

Deep learning methods have emerged as powerful tools for analyzing histopathological images, but current methods are often specialized for specific domains and software environments, and few open-source options exist for deploying models in an interactive interface. Experimenting with different deep learning approaches typically requires switching software libraries and reprocessing data, reducing the feasibility and practicality of experimenting with new architectures. We developed a flexible deep learning library for histopathology called Slideflow, a package which supports a broad array of deep learning methods for digital pathology and includes a fast whole-slide interface for deploying trained models. Slideflow includes unique tools for whole-slide image data processing, efficient stain normalization and augmentation, weakly-supervised whole-slide classification, uncertainty quantification, feature generation, feature space analysis, and explainability. Whole-slide image processing is highly optimized, enabling whole-slide tile extraction at 40x magnification in 2.5 s per slide. The framework-agnostic data processing pipeline enables rapid experimentation with new methods built with either Tensorflow or PyTorch, and the graphical user interface supports real-time visualization of slides, predictions, heatmaps, and feature space characteristics on a variety of hardware devices, including ARM-based devices such as the Raspberry Pi.
Authorship attribution in twitter: a comparative study of machine learning and deep learning approaches
Authors: Rebeh Imane Ammar Aouchiche, Fatima Boumahdi, Mohamed Abdelkarim Remmide, Amina Madani

As social media platforms gain popularity and influence, content integrity and user accountability issues become more critical. Authorship attribution (AA) is a powerful tool for tackling such issues by accurately determining the real author of online posts. This study proposes an AA approach using machine and deep learning algorithms to accurately predict the author of unknown posts on social media platforms. It introduces Temporal Convolutional Networks (TCN) for short texts, investigates the effectiveness of combining Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN), and explores the use of an Autoencoder combined with Adaboost classifier. This approach was tested on a Twitter dataset, achieving 52.77% accuracy in AA through multiple experiments across various scenarios.