An Effective Identification of Tuberculosis in Chest X-rays Using Convolutional Neural Network Model

Tuberculosis (TB) continues to be a major global health concern, contributing significantly to premature mortality rates around the globe. This research highlights the critical role that computational strategies play in enhancing diagnostic skills by showcasing notable gains in TB diagnosis accuracy through the use of an ensemble of heterogeneous CNN architectures. To detect TB in chest X-rays, the research that is being presented makes use of Convolutional Neural Network (CNN) models, namely DenseNet121 and ResNet50. Preprocessing techniques are used to improve the quality of the data before these models are trained on a dataset of chest X-ray pictures. The study illustrates the correctness and resilience of the suggested technique using a methodical procedure that includes processing datasets, choosing methods, testing, evaluating, and deploying them. This work is notable because it provides comprehensive explanations of model methodology, practical consequences, practicality, and requirement analysis, and it thoroughly examines numerous CNN architectures created specifically for tuberculosis detection. The study employs advanced deep learning algorithms and well-chosen datasets to attain high accuracy rates, as evidenced by the comparative analysis and visualization of training and validation metrics. This work not only shows the potential of CNNs in medical image processing, but it also significantly advances the ongoing battle against tuberculosis by facilitating accurate and timely diagnosis.