Skin Cancer Diagnosis Based on the Convolutional Neural ‎Network: a Comparative Study

Abstract

Skin cancer is one of the leading causes of death in humans, ‎however, it is treatable if caught early. Therefore, early detection of skin ‎cancer contributes to saving many patients. Skin cancer is divided into two ‎types, benign tumor, and malignant tumor that leads to the death of a person ‎if not treated early, and both are similar in appearance only a dermatologist ‎can classify cancer as malignant or benign. ‎The proposed system consists of several basic stages. The first stage ‎is the creation and provision of a large database, the second stage is the use ‎of data compression techniques (images), and the third stage is the use of ‎artificial intelligence by applying an artificial neural network to image ‎processing technology, specifically in the field of deep learning approach. ‎The database used in the proposed system consists of a set of skin cancer ‎images from the International Skin Imaging Cooperation (ISIC) and a set of ‎images also brought from the Medical City in Iraq (Dermatology ‎Consultation Department). Be clear and free of distortion at a good rate.‎Huffman technology is used to compression images while preserving ‎image information from loss, reducing image size, saving storage space, ‎saving time, and thus increasing system speed, as a neural network (deep ‎learning) was used with the SVM classifier for the support machine. Also, a ‎set of deep learning models VGG16, AlexNet, ResNet-50 and Inception v3 ‎were used only without any modifications to the models except the last layer ‎of each model. Finally, a special model that detects skin cancer (SkinNet) ‎was proposed.‎The method used in detecting skin cancer is deep learning that works ‎by inserting compressed images and then splitting the images 70% for the ‎training process, 30% for the testing process, and the proposed model ‎‎(SkinNet) performed better with accuracy, and the performance was with ‎‎98.2% accuracy.‎