Lung Diseases Prediction by Pattern Classification and Segmentation

Lung diseases are the maladies that affect the lungs, the organs that allow us to breathe and it is the most common medical conditions worldwide especially in India. The diseases such as pleural effusion and normal lung are detected and classified in this work. This paper introduces a computer aided classification method in Computer Tomography (CT) Images of lungs created utilizing ANN-BPN. The purpose of the work is to detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform [9] and GLCM Features. CT Images are used to segment the entire lung and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the ANN-Back Propagation Network designed for classification of ILD patterns. The parameters give the maximum classification Accuracy. After result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.