An Efficient Machine Learning Approach for Screening of COPD Lung Disease

Abstract

Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and the only chronic disease with increasing mortality rates. Progression of the disease is irreversible but can be stabilized or prevented by quitting. If COPD is detected earlier, the formation of lung cancer is prevented. In CT scan may provide additional information and also it provides more detailed images of parts of the body that cannot easily be seen on a standard chest radiograph. But the automatic screening process has lot of advantages such as decrease of labor, enhancing the sensitivity of the test and better precision in diagnosis by increasing the images that can be analyzed by the computer. Many researchers have proposed different techniques to improve the performance of automatic screening process. This paper involves in improving the accuracy over the existing technique using the adaptive region growing property and Extreme Learning Machine (ELM) classifier. Initially, pre-processing is carried out for the input image by Laplacian Gaussian filtering technique to make the image suitable for further processing. The contours of the image will be obtained using region growing technique. The ELM classifier is then used to confirm the suspected TB cavities. The classification will be carried out by the features which have been taken from the segmented image. The proposed technique is implemented in MATLAB and the performance is compared with the existing technique. From the experimental result it can be said that the proposed method achieved more accuracy as compared with existing techniques.