Abstract: Cutaneous diseases rank as a leading global health issue and many of them should be diagnosed in time to treat them appropriately. With the development of deep learning, automated skin disease diagnosis is now possible and has been improved to be more accurate. In this paper, we propose a deep-learning methodology based on the VGG16 CNN model for classifying skin diseases from the DERMNET dataset. Preprocessing and data augmentation steps are employed to enhance the robustness and generalization ability of the model. The above system effectively demonstrated a diagnostic accuracy of around 90% indicating that it can provide great support for dermatologists and reduce diagnostic errors. In addition, to provide real-time diagnostic assistance, a Streamlit tool is implemented as an interface to invoke the trained model.
Keywords: Deep Learning, Skin Disease Detection, VGG16, Convolutional Neural Network, Data Augmentation, Stream lit.
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DOI:
10.17148/IJARCCE.2025.14659
