Tuberculosis is one of the major lung disorders in the world. The major cause for TB is spreading through the air when people who have active TB in their lungs cough, spit, speak or sneeze. It occurs often in people with HIV/AIDS and in those who smoke. The process of diagnosing is still a great challenge, since it is based on the methods developed in the last century. If left untreated the death rate of TB patients was increase due to slow and unreliable diagnosing process. The aim of this paper is to design an automated approach for finding TB. Using binary classifier, the X-rays is categorized as normal and also classifies TB on five types in the lung region. TB Screening using CAD system is already available for field development that approaches the performance of human expert. Still diagnosing TB for HIV/AIDS Patients is a challenging task and multidrug resistant. Highly standard diagnosed is lacking in last century, so the patients are left. In order to diminish the risk of TB, a new method called “conventional poster anterior chest radiographs” for detection of TB is introduced. In this method, a region of lung is extracted using a graph cut segmentation method. In this extracted segment, a set of texture and shape features is computed. The area under an ROC curve for first time is referred by (AUC) of 87% and second set of 90%. TB is classified by the threshold values.