Skin cancer detection and classification

Skin cancer is the most common type of cancer, which affects the life of millions of people every year. About three million people are diagnosed with the disease every year in the United States alone. The rate of survival decreases steeply as the the disease progresses. However, detection of skin cancer in the early stages is a difficult and expensive process. In this study, we propose a methodology that detects and identifies skin lesions as benign or malignant based upon images taken from general cameras. The images are segmented, features extracted by applying the ABCD rule and a Neural Network is trained to classify the lesions to a high degree of accuracy. The trained Neural Network achieved an overall classification accuracy of 76.9% on a dataset of 463 images, divided into six distinct classes.