Tuberculosis detection in chest X-ray images using optimized gray level co-occurrence matrix features

Tuberculosis (TB) is a deadly infectious disease caused by Mycobacterium Tuberculosis (MTB). Chest X-ray (CXR) image has been the main tool for detecting lung TB historically. CXR images are analyzed by radiologists to determine whether or not there are signs of TB in the lungs. The results of the analysis by radiologists in analyzing CXR images are influenced by the subjectivity of radiologists, such as experience from radiologists, conditions of observation, fatigue, and others. The subjectivity factor of the radiologist can be overcome by the computer aided diagnosis system. This paper proposed a TB detection system on CXR images using optimized Gray Level Co-Occurrence Matrix (GLCM) features as the input. GLCM is optimized using the Principal Component Analysis (PCA) and then classified using the Support Vector Machine (SVM). In this paper, CXR images were classified as normal, primary TB (PTB) and secondary TB (STB). The results of this paper indicate that the classification system with optimized GLCM as input has better performance than the classification system with regular GLCM as input. The classification system with optimized GLCM as input in the 8-fold cross validation test has an accuracy of 100% for the normal class, 98.72% for the PTB class and 98.72% for the STB class.