Tuberculosis (TB) is the greatest irresistible illness in humans, caused by microbes Mycobacterium TB (MTB) bacteria and is an infectious disease that spreads from one individual to another through the air. It principally influences lung, which is termed Pulmonary TB (PTB). However, it can likewise influence other parts of the body such as the brain, bones and lymph nodes. Hence, it is also referred to as Extra Pulmonary TB (EPTB). TB has normal symptoms, so without proper testing, it is hard to detect if a patient has TB or not. In this paper, an accurate and novel system for diagnosing TB (PTB and EPTB) has been designed using image processing and AI-based classification techniques. The designed system is comprised of two phases. Firstly, the X-Ray image is processed using preprocessing, segmentation and features extraction and then, three different AI-based techniques are applied for classification. For image processing, ‘Histogram Filter’ and ‘Median Filter’ are applied with the CLAHE process to retrieve the segmented image. Then, classification based on AI techniques is done. The designed system produces the accuracy of 98%, 83%, and 89% for Decision Tree, SVM, and Naïve Bayes Classifier, respectively and has been validated by the doctors of the Jalandhar, India.

In this research work, an artificial intelligence (AI) based system has been designed for the accurate and early diagnosis of the Tuberculosis (TB). For carrying out this work, a comparative study of the literature has been done. It has been found that most of the work emphasized on the diagnosis of Pulmonary Tuberculosis and there is no significant work found for the diagnosis of Pulmonary Tuberculosis (PTB) as well as Extra Pulmonary Tuberculosis (EPTB). The proposed system is designed to detect PTB as well as EPTB using AI based learning techniques. The designed system is implemented in two phases. In the first phase, the X-Ray image is processed using image pre-processing, segmentation and features extraction. In the second phase, the classification is done using three different AI-based techniques. For image processing, ‘Histogram Filter’ and ‘Median Filter’ are applied with the CLAHE process to retrieve the segmented image. Then, classification based on Decision Tree, Naïve Bayes, and SVM has been done. The designed system produces the accuracy of 98%, 83%, and 89% for Decision Tree, SVM, and Naïve Bayes Classifier, respectively.