An Enhanced Hybrid Model for Skin Diagnosis Using Deep Convolution Neural Network

Abstract

Melanoma is the deadliest form of skin cancer. Unfortunately, Skin cancer can’t be identified by visual examination. So, there is a call for an automated model which assists dermatologists in early diagnosis of skin cancer and help remote patient to save their life by remote diagnosis. This paper introduces an enhanced expert computer-aided model for skin diagnosis using deep learning. The proposed region of interest (ROI) segmentation is done by integrating both color and texture properties for the skin in both spatial and frequency domains. Then, the convolution neural network (CNN) is used for extracting all the possible discriminating features. Experiments have been conducted on various large datasets to demonstrate the efficiency of the proposed model. The experimental results show an outstanding performance in the terms of sensitivity, specificity and accuracy compared with others in literature.