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<p>Accurate diagnosis and assessment of the severity of skin diseases are essential for appropriate clinical treatment. This paper proposes a multi-stage intelligent diagnosis framework based on deep learning to assist dermatologists in decision-making. The framework firstly adopts LeNet-5 convolutional neural network for preliminary classification of common skin diseases, and then performs secondary classification of disease severity and progression for selected representative conditions. Through manual annotation, clinical prior knowledge, including the predilectable location of the lesion, is incorporated into the framework to improve the reliability of the diagnosis. All images were preprocessed with grayscale conversion to reduce visual variability. Experimental results show that the performance of the proposed framework is stable and reliable, especially in the recognition tasks of disease severity and stage with obvious clinical manifestations. This hierarchical diagnostic strategy is consistent with routine clinical workflows and shows potential as an adjunct to precision diagnosis and treatment planning in dermatology.</p>

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Research on multi-stage deep learning based intelligent diagnosis of skin diseases and skin medicine diagnosis community construction concept

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月22日

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  • Junzhang Chen,
  • Fapeng Cai,
  • Weizhe Ding,
  • Dong Liang

摘要

Accurate diagnosis and assessment of the severity of skin diseases are essential for appropriate clinical treatment. This paper proposes a multi-stage intelligent diagnosis framework based on deep learning to assist dermatologists in decision-making. The framework firstly adopts LeNet-5 convolutional neural network for preliminary classification of common skin diseases, and then performs secondary classification of disease severity and progression for selected representative conditions. Through manual annotation, clinical prior knowledge, including the predilectable location of the lesion, is incorporated into the framework to improve the reliability of the diagnosis. All images were preprocessed with grayscale conversion to reduce visual variability. Experimental results show that the performance of the proposed framework is stable and reliable, especially in the recognition tasks of disease severity and stage with obvious clinical manifestations. This hierarchical diagnostic strategy is consistent with routine clinical workflows and shows potential as an adjunct to precision diagnosis and treatment planning in dermatology.