Deep Learning-Based Automatic Detection and Diagnosis of Tuberculosis from Chest X-ray Images: A Comprehensive Analysis

Tuberculosis (TB) continues to be one of the foremost public health issues in the world, and remains the second most salient communicable cause of death after COVID-19. In 2022, TB accounted for 10.6 million new infections and 1.3 million deaths globally. Conventional diagnostic approaches involving sputum smear microscopy, culture assays, and GeneXpert MTB/RIF are characterized by excessive turnaround times, elevated costs, and dependency on specialised infrastructure and trained personnel. These constraints are exacerbated in resource-poor countries, resulting in delayed diagnosis, delayed therapy initiation, and enhanced disease transmission. This work investigates the application of deep learning algorithms to automatically diagnose TB from chest X-ray images as a promising alternative method of diagnosis. The evolution of machine learning and deep learning technologies offers novel opportunities to address these diagnostic dilemmas because TB manifests apparent characteristics, such as pleural thickening, fibrosis, infiltration, masses, and nodules that are resolvable from chest X-ray images. We trained and tested four state-of-the-art convolutional neural networks (CNNs), that is, VGG16, ResNet50, InceptionV3, and DenseNet121, on a dataset of 4,200 chest X-rays with 700 positive TB cases and 3,500 normal cases. The approach comprises extensive data preprocessing, applying transfer learning techniques, balancing classes through weighted class consideration, and rigorous task evaluation using measures such as accuracy, precision, recall, and F1-score. DenseNet121 yielded the best-performing model with a total accuracy of 98.0% and balanced sensitivity and specificity between the two classes. The deep learning method proposed in this study holds great promise for enhancing the TB diagnosis accuracy, speed, and accessibility, particularly in resource-poor settings. This work finds critical applications in bridging the gap between diagnosing and treating TB and offers a scalable and cost-effective method for early diagnosis and prompt intervention in global TB control measures.