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

Saleh Albahli1,2

  • 1Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.

Diagnostics (Basel, Switzerland)
|March 28, 2025
PubMed
Summary
This summary is machine-generated.

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This study introduces a YOLOv8 deep learning model for accurate melanoma detection and segmentation, improving diagnostic speed and efficiency for skin cancer.

Area of Science:

  • Artificial Intelligence
  • Medical Imaging
  • Computational Biology

Background:

  • Melanoma diagnosis relies on subjective interpretation, leading to errors.
  • Accurate and timely diagnosis is critical for improving melanoma patient survival rates.
  • Existing diagnostic methods lack consistency and efficiency.

Purpose of the Study:

  • To develop a robust deep learning framework for real-time melanoma detection and segmentation.
  • To enhance the generalizability of melanoma detection models across diverse clinical conditions.
  • To improve diagnostic accuracy and computational efficiency in melanoma identification.

Main Methods:

  • Utilized a YOLOv8-based deep learning framework for unified detection and segmentation.
  • Employed a multi-dataset training strategy with ISIC 2020, HAM10000, and PH2 datasets.
Keywords:
YOLOv8deep learningmelanoma detectionskin lesion segmentation

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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  • Applied adaptive contrast enhancement, artifact removal, CutMix, and Mosaic augmentation.

    • Main Results:

    • Achieved state-of-the-art performance with 98.6% mAP@0.5, 0.92 Dice Coefficient, and 0.88 IoU.
    • Outperformed conventional models like U-Net, DeepLabV3+, Mask R-CNN, SwinUNet, and SAM.
    • Demonstrated real-time inference speeds of 12.5 ms per image.

    Conclusions:

    • The YOLOv8 framework offers high accuracy and efficiency for melanoma diagnosis.
    • Multi-dataset training is crucial for robust model generalization.
    • Integrating explainable AI can enhance clinical trust and interpretability.