A Robust YOLOv8-Based Framework for Real-Time Melanoma Detection and Segmentation with Multi-Dataset Training.

IF 3 3区 医学 Q1 MEDICINE, GENERAL & INTERNAL
Saleh Albahli
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引用次数: 0

Abstract

Background: Melanoma, the deadliest form of skin cancer, demands accurate and timely diagnosis to improve patient survival rates. However, traditional diagnostic approaches rely heavily on subjective clinical interpretations, leading to inconsistencies and diagnostic errors. Methods: This study proposes a robust YOLOv8-based deep learning framework for real-time melanoma detection and segmentation. A multi-dataset training strategy integrating the ISIC 2020, HAM10000, and PH2 datasets was employed to enhance generalizability across diverse clinical conditions. Preprocessing techniques, including adaptive contrast enhancement and artifact removal, were utilized, while advanced augmentation strategies such as CutMix and Mosaic were applied to enhance lesion diversity. The YOLOv8 architecture unified lesion detection and segmentation tasks into a single inference pass, significantly enhancing computational efficiency. Results: Experimental evaluation demonstrated state-of-the-art performance, achieving a mean Average Precision (mAP@0.5) of 98.6%, a Dice Coefficient of 0.92, and an Intersection over Union (IoU) score of 0.88. These results surpass conventional segmentation models including U-Net, DeepLabV3+, Mask R-CNN, SwinUNet, and Segment Anything Model (SAM). Moreover, the proposed framework demonstrated real-time inference speeds of 12.5 ms per image, making it highly suitable for clinical deployment and mobile health applications. Conclusions: The YOLOv8-based framework effectively addresses the limitations of existing diagnostic methods by integrating detection and segmentation tasks, achieving high accuracy and computational efficiency. This study highlights the importance of multi-dataset training for robust generalization and recommends the integration of explainable AI techniques to enhance clinical trust and interpretability.

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来源期刊
Diagnostics
Diagnostics Biochemistry, Genetics and Molecular Biology-Clinical Biochemistry
CiteScore
4.70
自引率
8.30%
发文量
2699
审稿时长
19.64 days
期刊介绍: Diagnostics (ISSN 2075-4418) is an international scholarly open access journal on medical diagnostics. It publishes original research articles, reviews, communications and short notes on the research and development of medical diagnostics. There is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodological details must be provided for research articles.
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