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This paper proposes Revolutionizing Liver Lesion Detection with Advanced YOLOv12 Segmentation. In general, this paper is well presented. The following issues can be further considered.

1.	More background and motivation of this study can be added, in case the readers are not very familiar with the topic.


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Thank him and update 

1.	Introduction
Liver diseases, encompassing conditions such as liver abscesses, cysts, and tumors, represent a significant global health burden, necessitating accurate and timely diagnosis for effective treatment planning. The early detection of liver lesions is critical for improving patient outcomes, yet the current reliance on manual interpretation of medical images poses considerable challenges. Radiologists must navigate low-contrast imaging, irregular lesion boundaries, and significant intra-class variability, all of which contribute to a time-consuming and error-prone diagnostic process. Furthermore, the visual similarities between different types of liver lesions, such as texture, shape, and intensity levels, complicate the task of accurate classification. These challenges underscore the need for advanced diagnostic tools that can enhance the precision and efficiency of liver lesion detection [1] [2] [3].
Traditional diagnostic methods heavily depend on the expertise of radiologists, introducing subjectivity and variability in lesion identification and classification. This variability can lead to inconsistencies in diagnosis, potentially delaying critical interventions. Moreover, the increasing volume of medical imaging data further strains the capacity of healthcare systems, highlighting the urgent need for automated solutions. Recent advancements in deep learning and computer vision have opened new avenues for addressing these challenges, offering the potential to automate the detection and segmentation of liver lesions with high accuracy. However, the performance of these automated systems is highly contingent on the availability of large, well-annotated datasets, which are often scarce in the medical domain due to the complexity and cost of annotation [4] [5] [6].
To address this gap, the LIVERSCANPLUSPLUS - v17 dataset has been developed, providing a standardized and augmented collection of liver lesion images annotated in the YOLOv12 format. This dataset comprises 822 images categorized into three distinct classes: Liver Abscess, Liver Cyst, and Liver Tumor. By offering a diverse and well-annotated dataset, LIVERSCANPLUSPLUS - v17 aims to facilitate the development and evaluation of deep-learning models for liver lesion detection and segmentation. The dataset undergoes pre-processing and augmentation to enhance model generalization, ensuring robust performance across varying imaging conditions and lesion types.
This study focuses on leveraging YOLOv12, a state-of-the-art object detection and segmentation model, to improve the accuracy and efficiency of liver lesion detection. YOLOv12 builds on the strengths of previous YOLO (You Only Look Once) models, offering real-time detection capabilities with high precision. By training and fine-tuning YOLOv12 on the LIVERSCANPLUSPLUS - v17 dataset, this research aims to develop a robust model capable of addressing the challenges posed by low-contrast medical images, irregular lesion boundaries, and inter-class similarities. The ultimate goal is to contribute to the early diagnosis of liver diseases, reduce the burden on radiologists, and improve patient outcomes through automated medical imaging analysis.

Datasets 
Dataset 1 Description: The LIVERSCANPLUSPLUS - v17 dataset addresses this need by providing a standardized, annotated, and augmented collection of liver lesion images. This dataset is designed to facilitate research in medical imaging and computer vision. It is publicly available and includes detailed annotations, pre-processing, and augmentation to support the development of accurate and generalizable models. The LIVERSCANPLUSPLUS - v17 dataset consists of 822 images annotated for liver lesion detection. The dataset is divided into three subsets for training, validation, and testing, as shown in Table 5.
Table 5: Dataset Splits
Subset	Percentage	Number of Images	Purpose
Training Set	87%	717	Model training and parameter tuning
Validation Set	9%	70	Hyperparameter optimization
Test Set	4%	35	Final model evaluation
The dataset includes three classes of liver lesions, as described in Table 6.
Table 6: Dataset Classes
Class Name	Description	Example Use Case
Liver Abscess	A collection of pus within the liver	Diagnosing infectious liver diseases
Liver Cyst	A fluid-filled sac in the liver	Identifying benign liver conditions
Liver Tumor	Abnormal growth of liver tissue	Detecting malignant liver lesions


1.1.1	Pre-processing
To ensure consistency and quality, each image in the dataset underwent the following pre-processing steps:
1.	Auto-orientation: Pixel data was auto-oriented, and EXIF orientation metadata was stripped to standardize image orientation.
2.	Resizing: Images were resized to a fixed resolution of 640x640 pixels using a stretch method, ensuring uniformity across the dataset.
3.	Data Augmentation
To enhance the dataset's diversity and robustness, three augmented versions of each source image were generated using the following techniques:
•	Flip: 50% probability of horizontal or vertical flipping.
•	Rotation: Equal probability of 90-degree rotations (none, clockwise, counter-clockwise, or upside-down).
•	Cropping: Randomly cropped between 0% and 20% of the image.
•	Rotation: Random rotation between -15° and +15°.
•	Shear: Random shear between -15° and +15° horizontally and vertically.
•	Blur: Random Gaussian blur of up to 2.5 pixels.
•	Noise: Salt and pepper noise applied to 0.5% of pixels.
•	These augmentations simulate real-world variations in imaging conditions, improving the generalization capability of models trained on this dataset. Table 7 provides a summary of the augmentation techniques applied.
Table 7: Augmentation Techniques
Augmentation Type	Parameters/Details	Purpose
Flip	50% probability (horizontal or vertical)	Simulate different viewing angles
Rotation	90° rotations (none, clockwise, counter-clockwise, upside-down)	Increase orientation diversity
Cropping	Randomly crop 0-20% of the image	Simulate partial views of lesions
Random Rotation	Between -15° and +15°	Add minor orientation variations
Random Shear	±15° horizontally and vertically	Simulate perspective distortions
Gaussian Blur	Up to 2.5 pixels	Simulate out-of-focus images
Salt and Pepper Noise	Applied to 0.5% of pixels	Simulate sensor noise
5. Availability
The dataset is publicly accessible via Mendely Datasets at the following link: https://data.mendeley.com/datasets/3cdj6vfr4y/1 . 
Figure 2 presents a comprehensive overview of liver disease classes, accompanied by representative sample images for each category.
Class Name	Sample Images
Liver Abscess	 

Liver Cyst	 
Liver Tumor	 
Liver Normal	 
Figure 2: Sample Images of Plant Diseases.

Dataset 2 Description
Dataset 2 consists of 3,397 images designed for medical imaging applications, particularly in liver tumor detection and segmentation. The dataset is structured to support machine learning workflows, with a standard split into training, validation, and test sets. The images have undergone basic preprocessing to ensure uniformity, making them suitable for deep learning models in computer-aided diagnosis (CAD) systems.
The dataset is divided into 2,378 training images (70%), 679 validation images (20%), and 340 test images (10%). This distribution ensures sufficient data for model training while maintaining reliable validation and test sets for performance evaluation. The split follows best practices in machine learning, allowing researchers to develop and fine-tune models effectively.
All images have been auto-oriented to correct their alignment and resized to 640×640 pixels using a stretch method. While this ensures consistent dimensions, it may introduce distortions if the original images had varying aspect ratios. Notably, no additional augmentations (such as rotations, flips, or contrast adjustments) were applied, meaning users may need to incorporate these techniques during training to enhance model robustness.
The dataset is available on:
•	Roboflow Universe:  https://universe.roboflow.com/segmentasiliver/project-5c-liver-tumor-aikum/dataset/1 
•	Mendeley Data (alternative link) :  https://data.mendeley.com/datasets/bdszxg5kmp/1 
These resources may provide additional images, annotations, or varied preprocessing techniques that complement Dataset 2. Researchers working on liver pathology, radiomics, or AI in healthcare may find these links valuable for comparative studies or expanded training data.