Abstract
Abstract: One of the top 10 main causes of mortality is tuberculosis (TB), a bacterial infection-related chronic lung disease. A bacteria known as Mycobacterium tuberculosis is the cause of the infectious illness tuberculosis (TB). TB must be accurately and quickly identified in order to be treated; else, it might be fatal. Chest X-rays (CXR) are frequently utilized for pulmonary TB identification and screening. Chest radiographs are evaluated for the presence of TB by skilled doctors in clinical practice. But this is a subjective and time-consuming procedure. It's important to note that CXR pictures of TB are frequently misclassified to other diseases with similar radiologic patterns, which may cause patients to receive the wrong medicine, deteriorating their health. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques.
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