Classification of skin cancer and benign lesions using independent component analysis

摘要

A neural network model for the identification and classification of different skin lesions from ALA-induced fluorescence images, is presented. After various image preprocessing steps, eigenimages/independent base images are extracted using PCA and ICA. In order to use local information in the images rather than global features, self organizing maps are added to cluster patches of the images first and then extract local features by ICA (local ICA). These components are used to distinguish skin cancer from benign lesions. An average classification rate of 70% is achieved, which considerably exceeds the rate achieved by an experienced physician.