Skin Cancer Classification using SkinNet

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Abstract

Addressing the critical health problem of skin cancer, rapid and precise lesion detection is imperative for powerful treatment. The model concentrates on identifying seven specific skin most cancers types: Melanoma, Basal cell carcinoma, Benign keratosis-like lesions, Dermato-fibrosarcoma protuberans, Actinic keratosis, Melanocytic nevi, and Vascular lesions. The proposed model, SkinNet is a specialized model designed to enhance an accurate classification of lesions and skin cancer diseases. The implementation of SkinNet achieved an accuracy rate of 98%, marking a significant advancement in healthcare with promising implications for both healthcare providers and patients in the future.

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Keywords

Machine Learning, Deep Learning, Lesion Classification, Convolutional Neural Network.