Inception V3 model for Tuberculosis detection using chest x-ray images

The respiratory disease tuberculosis is extremely deadly and morbid. Mycobacterium tuberculosis, which can infect many tissues, is the chronic disease that causes illness. Deep learning reveals value for a range of aspects of infectious diseases surveillance and detection, including that of the identification of tuberculosis. By use of a collected from various sources dataset on tuberculosis, a convolutional neural network model to evaluate the analysis and interpretation of the Inception-V3 deep learning model. By using data augmentation, batch normalization and deep learning classification algorithms, this research was able to accurately identify tuberculosis disease from chest X-ray photos. Transfer learning was utilized to train, assess, and evaluate InceptionV3 (CNN) model for the classification of tuberculosis and healthy cases. The Inception-V3 model which was accomplished got the best score, with an accuracy rate of 99%.