Using Deep Learning Architectures For Skin Cancer Classification

Abstract

Since skin cancer is one of the most common types of cancer, prompt diagnosis is essential to successful treatment. Impressive performance in image-based classification tasks has been demonstrated by convolutional neural networks (CNNs), particularly in recent years. In this study, the proposed CNN model was applied to the ISIC skin cancer classification challenge. A proposed deep learning model and four popular deep CNN models (ResNet, GoogleNet, AlexNet, and VGG16) were used to classify the skin cancer images. High levels of accuracy on test data from the ISIC dataset were achieved by the proposed CNN model, according to experimental results. Preprocessing was performed on images with sizes of 64x64, 100x100, 224x224, and 128x128 pixels. The experimental results show that the proposed CNN model achieved the highest accuracy rate of 86.76% on 128x128 size images.