An Efficient ANFIS Based Approach for Screening of Chronic Obstructive Pulmonary Disease from Chest CT Scans with Adaptive Median Filtering

Medical diagnostic and imaging system are ubiquitous in modern health care facilities. The advantages of early detection of potential lesions and suspicious masses within the bodily tissue have been well established. It can be detected and assessed many different types of injuries, diseases, and conditions with the aid of the medical imaging that allows medical personnel to look into living cells non-instructively. Chronic Obstructive Pulmonary Disease (COPD) is the fourth leading cause of death worldwide and the only chronic disease with increasing mortality rates. COPD is the name for a group of lung diseases including chronic bronchitis, emphysema and chronic obstructive airways disease. This paper involves in improving the accuracy over the existing technique using the adaptive region growing property and Adaptive-Neuro-Fuzzy Inference System-ANFIS classifier. Initially, pre-processing is carried out for the input image by Adaptive median Filter technique to make the image suitable for further processing. The contours of the image will be obtained using region growing technique. The ANFIS classifier is then used to confirm the suspected COPD 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.