Skin Lesion Classification Using Convolution Neural Networks Rajasekhar K.S.1,*, Babu T. Ranga2 1Research Scholar, Department of ECE, ANUCET, ANU 2Professor, Department of ECE, RVR & JC Engineering College, Guntur, Andhra Pradesh, India *Corresponding Author: K.S. Rajasekhar Assistant Professor, Dept. of ECE, ANUCET, Acharya Nagarjuna University, e-mail: rajsekharkotra@gmail.com
Online published on 31 March, 2020. Abstract Skin cancer is one of the deadliest disease found in humans. These skin cancers are of various types like Basal Cell Carcinoma(BCC), Melanoma, Nevus, Seborrheic Keratosis (SK), Squamous Cell Carcinoma (SCC). Some of the skin cancers can be identified visually, but in order to diagnose a skin cancer patient should have to undergo for a biopsy test and it takes a long time to diagnose. To overcome this an automated skin lesion classification system has to be developed. In this work, a basic architecture of the Convolution Neural Network(CNN) model is used to classify different skin lesions. The proposed model achieved better accuracy for SCC Vs SK, BCC Vs SK, Melanoma Vs Nevus and Melanoma Vs SK are 0.9741, 0.9867, 0.9506 and 0.9734 respectively for 25 epochs when compared to the other related works. Top Keywords Skin cancer, Basal Cell Carcinomsa, Melanoma, Nevus, Seborrheic Keratosis, Squamous Cell Carcinoma, CNN, Training Accuracy, Validation Accuracy. Top |