An Improved Deep CNN For an Early and Accurate Skin Cancer Detection and Diagnosis System

Abstract

Skin cancer is considered to be the most common and dangerous type of cancer. Information technology techniques are required to detect and diagnose skin cancer. Therefore, there is a need for an early and accurate skin cancer diagnosis and detection by employing an efficient deep learning technique. This research work proposes automatic diagnosis of skin cancer by employing Deep Convolution Neural Network (DCNN). The distinguishing feature of this research is it employs DCNN with 12 nested processing layers increasing the diagnosis and detection of skin cancer accuracy. Beside neural network, machine learning techniques of naïve Bayes and random forest are also utilized to detect skin cancer. This research work results concluded that the deep learning technique are more effective than machine learning in terms of skin cancer detection. By applying Naïve Bayesian on the proposed system accuracy of 96% were achieved, similarly for Random Forest method, an accuracy of 97% were achieved. The accuracy of 99.5% were achieved by applying Deep CNN network. The performance of proposed system has been compared with other research work and it is concluded that it shows the higher performance compared to all conventional systems.