Skin cancer is common and dangerous. This illness, like other cancers, requires early detection. Conventional skin cancer diagnosis methods are inaccurate and can lead to unnecessary inspections. Certain cancer detection machine learning algorithms support a limited number of skin cancer classifications, which might be a drawback. The research method can automatically detect skin cancer and benign tumor lesions using the Convolutional Neural Network. The model contains three hidden layers with 16–32–64 output channels. The model uses SGD, RMSprop, Adam, and Nadam optimizers with a learning rate of 0.001. The Adam optimizer classifies ISIC dataset skin lesions as benign or malignant with 93% accuracy. The results outperform the current skin cancer classification approach. This study uses ISIC [24].

Skin Cancer , ISIC , Convolutional Neural Network, Adam, and Nadam.