LUNG PATTERN CLASSIFICATION FOR INTERSTITIAL LUNG DISEASES USING AN ARTIFICIAL NEURAL NETWORK-BACK PROPAGATION

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LUNG PATTERN CLASSIFICATION FOR INTERSTITIAL LUNG DISEASES USING AN ARTIFICIAL NEURAL NETWORK-BACK PROPAGATION

Abstract

Lung is the organ that allows us to breathe and lung disease are the disorders that affect the lungs. This paper presents a computer aided classification Method in Computer Tomography (CT) Images of lungs developed using 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 (DTCWT) and Gray Level Cooccurrence Matrix (GLCM) Features. The entire lung is segmented from the CT Images and the parameters are calculated from the segmented image. The parameters are calculated using GLCM. We Propose and evaluate the Artificial Neural Network -Back Propagation method designed for classification of Interstitial Lung Disease (ILD) patterns. We collect different types of CT images of lung and train artificial neural network. The parameters give the maximum classification Accuracy. After the result we propose the Fuzzy clustering to segment the lesion part from abnormal lung.

Author

SANGEETHA R VITHYA V SUBASHREE K R RAJAMANI A

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International Journal of Advanced
Science and Engineering Research

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