Automated Classification of Tuberculosis by PSO based Machine Learning using Chest Radiographs

Abstract

Classification of tuberculosis based on chest X-Rays is the most feasible and faster when compared with other alternate slow and unreliable methods like Sputum smear microscopy. This paper presents an automated method for identifying TB in chest radiographs. Initially the chest Xray images are segmented by nature inspired multilevel PSO based segmentation method. From the segmented images textural feature vectors are calculated using gray-level cooccurrence matrix. This feature vector is used for classifying the chest X-rays into normal and abnormal using neural network classifier. Performance of this system is measured on the data set collected from various diagnostic centers in Chennai, India. 75% of the data set are used for training while25 % datas in the dataset is used for testing the classifier. The accuracy achieved is greater than 90% for each level of segmentation. The accuracy is compared with other existing systems