Neural image recognition system with application to tuberculosis detection

Now a days, image recognition systems have several applications in enormous fields. The use of recognition systems (based on artificial neural networks) as means of predicting medical diagnosis and recommending successful treatments has been a highly active research field in past five years. We construct and train an artificial neural network to serve as a knowledge base that can accurately detect pulmonary tuberculosis. The first necessary step is to preprocess the different patients MMR´s, which consisted of lesions of tuberculosis and extract the features. Then the extracted features are converted into usable format (gray scale values) and given to neural net for training. It is based on back propagation algorithm. Then the knowledge base is used to detect a sample collected from a new patient is given as target to recognize it.