Automatic Detection of Melanoma and Non Melanoma Skin Cancer: Using Classification Framework of Neural Network

Abstract

In Automatic Detection of Melanoma and Non Melanoma skin Cancer, the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing and compare the result with two method Discrete wavelet transformation and lifting wavelet transformation. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 90.2% in LWT and 89.1% in DWT method. Auto-associative neural network is 81.2% in LWT method and 80.2% in DWT method .The image in database include dermoscopy photo and digital photo.