Abstract
Skin cancer is the far most common type of cancer. This can be treated effectivelyif it found early. The cancer detection in early stages is very expensive. Skin cancer is theabnormal growth of the skin cells. These are highly curable when identified and treated early. There are four types of skin cancers: Actinic Keratoses (AK), Basal cell carcinoma (BCC), Dermatofibroma and Melanoma. The late identification of cancer leads to the spread over other organs. The skin cancer can be detected from the images using convolution neural networks. ISIC image dataset and HAM10000 dataset will be used in this implementation. Transfer learning improves the performance of the model in CNN's.Pre trained models are used to extract features, which further used to classify types of skin cancer. The machine learning and deep learning methods used in this implementation are Random Forest, SVM, CNN and Dense net.
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