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We propose a method for identifying and classifying skin diseases using the HAM10000 dataset, which contains approximately 10,015 images from ISIC. The approach combines the ResNet-50 neural network for feature extraction with enhanced random forest (ERF) for classification. Compared to traditional algorithms, our model accurately identifies lesion edges and improves classification reliability. The proposed method achieved 95% accuracy on the HAM10000 dataset, outperforming existing algorithms and demonstrating its potential for reliable, automated skin disease detection.

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Skin Disease Detection Using ResNet-50 and Machine Learning-Based Enhanced Random Forest Approach

  • 会议论文

  • 发表日期：

    2025年

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  • Soujenya Voggu,
  • Shadab Siddiqui

摘要

We propose a method for identifying and classifying skin diseases using the HAM10000 dataset, which contains approximately 10,015 images from ISIC. The approach combines the ResNet-50 neural network for feature extraction with enhanced random forest (ERF) for classification. Compared to traditional algorithms, our model accurately identifies lesion edges and improves classification reliability. The proposed method achieved 95% accuracy on the HAM10000 dataset, outperforming existing algorithms and demonstrating its potential for reliable, automated skin disease detection.