Abstract—Tuberculosis (TB) is a serious infectious disease that mainly affects the human lungs. The bacteria that cause TB are spread via minute droplets released into the air via sneezes and/or coughs. A bacterium called Mycobacterium is the root cause of TB. This paper is to investigate the precision of four factors of detecting Pulmonary Tuberculosis based on the patients’ chest X-ray images (CXR) using Convolutional Neural Networks (CNN). We evaluate image dataset resolution, and then the pre-trained networks (AlexNet, VGG16 and VGG19) and various hyperparameter changes are investigated. Finally, additional sample images are tested and investigated. Simulations have been carried out based on 406 normal images & 394 abnormal images. Later an additional 239 normal images and 554 abnormal images are added. It is found that the splitting of images yielded the best results.
 

Index Terms—artificial intelligence, DICOM, tuberculosis, pulmonary

Cite: Michael J. Norval, Zenghui Wang, and Yanxia Sun, "Evaluation of Image Processing Technologies for Pulmonary Tuberculosis Detection Based on Deep Learning Convolutional Neural Networks," Journal of Advances in Information Technology, Vol. 12, No. 3, pp. 253-259, August 2021. doi: 10.12720/jait.12.3.253-259

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