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An Improved YOLOv8-Based Object Detection Algorithm for Skin Diseases.

Xinglong Yu1

  • 1Hebei Vocational University of Technology and Engineering.

Critical Reviews in Biomedical Engineering
|March 3, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced YOLOv8n algorithm for improved skin disease detection. The new method effectively addresses challenges like irregular lesion shapes and class imbalance, achieving higher accuracy in diagnosing skin conditions.

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Medical Imaging

Background:

  • Accurate skin disease diagnosis is vital but challenged by irregular lesion morphology, class imbalance, and complex imaging conditions.
  • Existing algorithms often struggle with these complexities, impacting clinical decision-making.

Purpose of the Study:

  • To develop an improved skin disease detection algorithm addressing current diagnostic challenges.
  • To enhance the accuracy and robustness of automated skin lesion analysis.

Main Methods:

  • An improved skin disease detection algorithm based on YOLOv8n was developed.
  • Key innovations include deformable large kernel attention (D-LKA), DCNv3 deformable convolutions, and an EMA-Slide Loss function.
  • The algorithm was evaluated on the International Skin Imaging Collaboration (ISIC) dataset.

Main Results:

  • The algorithm achieved 96.58% mAP50 and 88.32% mAP50-95 on the ISIC dataset, outperforming the baseline YOLOv8n.
  • Demonstrated a real-time inference speed of 31 ms per image, suitable for edge devices.
  • The method effectively handles irregular lesion morphologies and class imbalance.

Conclusions:

  • The proposed algorithm offers a clinically actionable tool for automated skin lesion analysis.
  • It significantly improves diagnostic accuracy and robustness compared to the standard YOLOv8n.
  • The algorithm is suitable for real-time applications and extensible for rare disease types and multi-modal data.