Development of algorithm for identification of maligant growth in cancer using artificial neural network

The precise identification and characterization of small pulmonary nodules at low-dose CT is a necessary requirement for the completion of valuable lung cancer screening. It is compulsory to develop some automated tool, in order to detect pulmonary nodules at low dose ct at the beginning stage itself. The various algorithms had been proposed earlier by many researchers within the past, but the accuracy of prediction is usually a challenging task. During this work, a man-made neural networ based methodology is proposed to seek out the irregular growth of lung tissues. Higher probability of detection is taken as a goal to urge an automatic tool, with great accuracy. The best feature sets derived from Haralick Gray level co occurrence Matrix and used because the dimension reduction way for feeding neural network. During this work, a binary Binary classifier neural network has been proposed to spot the traditional images out of all the images. The potential of the proposed neural network has been quantitatively computed using confusion matrix and located in terms of accuracy.