Skin disease recognition using texture analysis

Abstract

This research describes skin disease recognition by using neural network which based on the texture analysis. There are many skin diseases which have a lot of similarities in their symptoms, such as Measles (rubeola), German measles (rubella), and Chickenpox etc. In general, these diseases have similarities in pattern of infection and symptoms such as redness and rash. Diagnosis and recognition of skin disease take a very long term process because it requires patient's history, physical examination and proper laboratory diagnostic tests. Not only that, it also requires large number of features clinical as well as histopathological for analysis and to provide further treatment. The disease diagnosis and recognition becomes difficult as the complexity and number of features of the disease increases. Hence, a computer aided diagnosis and recognition system is introduced. Computer algorithm which contains few steps that involves image processing, image feature extraction and classification of data have been implemented with the help of classifier such as artificial neural network (ANN). The ANN can learn patterns of symptoms of particular diseases and provides faster diagnosis and recognition than a human physician. Thus, the patients can do the treatment for the skin disease faced immediately based on the symptoms detected.