School of Computer Engineering (November 2021)
1. Purohit, S., Suman, S., Kumar, A., Sarkar, S. Pradhan, C., and Chatterjee J. (2021), Comparative Analysis for Detecting Skin Cancer Using SGD-based Optimizer on a CNN versus DCNN Architecture and ResNet-50 versus AlexNet on Adam Optimizer in Deep Learning for Personalized Healthcare Services, Vishal Jain, Jyotir Moy Chatterjee, Hadi Hedayati (Eds.), De Gruyter, pp. 185–203.
This work presents a deep learning technique to classify images and detect skin cancer at an early stage. We have trained our model using images of harmless, that is benign, images and tumor-based images; we have used convolutional neural network on those images to classify whether the image is a suspect of skin cancer or not. This proposed approach achieved an accuracy of 86% and is compared to the DCNN model. Also, an additional approach using the ResNet-50, a 50-layer deep CNN has been implemented which has proved useful in further improving the accuracy to over 90%.