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

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 in the early stage. It also puts forward a conceptual feasible model for developing an integrated self-service diagnosis and medication dispensing community in the future mature stage. 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 conforms to routine clinical workflows and holds potential as a tool for precise diagnosis and treatment planning in dermatology.