Analysing the Performance of Dense Net in the context of Tuberculosis Disease

Tuberculosis (TB) is an infectious disease caused by the elusive Mycobacterium tuberculosis, leading to a persistent lung condition due to bacterial infection. The identification of tuberculosis (TB) is crucial, and chest X-ray images are routinely employed; nevertheless, manual interpretation is time-consuming and prone to errors. This research looked at; we introduced an advanced deep learning approach for classifying TB using chest X-rays. Our method involves leveraging deep learning algorithms to explore and identify optimal hypotheses for accurate predictions of TB. To address challenges like vanishing gradients and maximize feature reuse, we adopted the DenseNet architecture, well-known for its capabilities. This proposed model builds upon previous research, enhancing the accuracy of TB prediction using DenseNet, a powerful deep learning technique. The developed classifier, utilizing deep learning, achieved an impressive accuracy rate of 96%. This advancement holds promise for more efficient and reliable TB diagnosis through automated analysis of chest X-ray images.