A proposed architecture for convolutional neural networks to detect skin cancers

Abstract

The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the images used which are not classified and randomly taken from the set of images, and the final test data contains 1,500 anonymous images. An architecture for the convolutional neural network technology in deep learning has been proposed that consists of a set of layers for processing. Processing raw input images for a group of pre-treatment transformations, the data augmentation process, so the number of images used became 86094 images of nevus, 27,072 images of BCC, and 47,448 images of melanoma. Through the detection process, the classification and detection accuracy of BCC was 98.25%, which is higher than the classification accuracy of melanoma is 91.61%.