Lung Pattern Classification for Interstitial Lung Disease using ANN-BPN and Fuzzy Clustering
P. Santhoshini, R.M.D Engineering College, Sathiesh. K, R.M.D Engineering College
Segment Lesion, Fuzzy Clustering, DTCWT, ANN-BPN
The lungs are the primary organs of respiration in humans. The function of the respiratory system is to extract oxygen from the atmosphere and transfer it into the bloodstream, and to release carbon dioxide from the bloodstream into the atmosphere, in a process of gas exchange.The tissue of the lungs can be affected by a number of diseases, including pneumonia and lung cancer. Chronic diseases such as chronic obstructive pulmonary disease and emphysema can be related to smoking or exposure to harmful substances. Diseases such as bronchitis can also affect the respiratory track. The diseases such as pleural effusion and normal lung are detected and classified. computer aided classification Method in Computer Tomography (CT)presents the Images of lungs developed using ANN-BPN. To detect and classify the lung diseases by effective feature extraction through Dual-Tree Complex Wavelet Transform and GLCM Features. The entire lung is segmented from the CT Images and the parameters like Sensitivity, Specificity, Accuracy are calculated from the segmented image using GLCM. ANN-Back Propagation Network is designed for classification of ILD patterns. It can be achieved by neural network training tools. The parameters give the maximum classification Accuracy. Finally, the Fuzzy clustering is used to segment the lesion part from abnormal lung. It can be given by Performance metrics chart. This chart contains various progressions like epoch, time, performance, gradient, mu, validation check values.
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Paper ID: GRDJEV02I050216
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 298 - 304
Published in: Volume : 2, Issue : 5
Publication Date: 2017-05-01
Page(s): 298 - 304