Abstract
A deep learning-based binary classifier was proposed to diagnose tuberculosis (TB) and non-TB disease using a chest X-ray radiograph. The proposed classifier comprised two-step binary decision trees, each trained by a deep learning model with convolution neural network (CNN) based on the PyTorch frame. Normal and abnormal images of chest X-ray was classified in the first step. The abnormal images were predicted to be classified into TB and non-TB disease by the second step of the process. The accuracies of first and second step were 98% and 80% respectively. Moreover, re-training could improve the stability of prediction accuracy for images in different data groups.
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