Artificial Neural Network Implementation for Classification of Lung Tissues in High Resolution Tomography Images

Abstract

In this digital world, the amount of information what we are getting is uncountable and speed at which it reaches us is unpredictable. We are getting is uncountable. In order to understand and predict the required data we should have the image classification technique otherwise there will be lot of confusion and misunderstanding will happen during its study. Content based image retrieval technique can be used to exploit the properties of high resolution computed tomography data which will be stored in the achieve by identifying the similar images which will helped for radiologist for self learning and differential diagnosis of intestinal lungs diseases. This technique identifies the particular image based on the content present in the image rather than keywords or tag related to the image. The content present in the image means, patterns, colors, textures etc. high resolution technique of image identification method is used in many medical applications like identifying diseases or the identification of symptoms of the diseases. in this project we are using the neural network classifier to classify the lung tissues in high resolution computed tomography images. This method gives the proper and clear classification of the lung tissues which will helps in medical science field. This neural network classifier uses the optimal subset feature to classify the pattern of the lung tissues.