Performance of Multi Layer Perceptron and Deep Neural Networks in Skin Cancer Classification

Skin cancer refers to a condition where there exists abnormal growth of skin cells, mostly occurs on skin exposed to the sun. There are several types of skin cancer, where the most common types include basal cell carcinoma, squamous cell carcinoma, and melanoma. Without proper treatment, skin cancer, particularly in the melanoma form, can lead to deaths. Fortunately, early detection and classification of skin cancer are highly effective in preventing serious damages from skin cancer. In this paper, we train Multi-layer Perceptron, a custom convolutional neural network, and VGG-16 for skin cancer classification on a large skin cancer dataset, HAM10000. The performance of each trained model is subsequently compared and analyzed in terms of classification accuracy and computational time. Our experimental setups reveal that the VGG-16 model can set the best classification accuracy among the compared networks while in terms of testing time, the VGG-16 and custom CNN models are being much faster than the Multi-layer Perceptron. The results of our study are beneficial in providing systematic comparison and analysis of several neural networks in skin cancer classification.