Machine Learning-Aided Screening of Tuberculosis in Chest X-Rays.

Abstract

As a highly infectious and contagious disease, Tuberculosis(TB) still remains a major threat to global health especially in low and middle income countries such as Uganda and ranks highest in the causes of death from a single infectious agent. A very important step in the TB diagnosis and treatment is screening and early detection of the disease, with chest x-ray ( CXR) being the mostly widely used tool. However, there are challenges of limited number of radiologists and inherent human error during CXR interpretation. In an effort to solve this problem, there has been a significant rise in the interest to use artificial based intelligence-based TB screening tools.

In this work, we explored the use of deep learning techniques in the automatic detection of TB, not only distinguishing TB positive from health but also pneumonia and COVID-19 that usually manifest in a similar manner. A dataset of 700 TB images and 720 images for each of the other classes was accrued from two publicly available datasets. Transfer learning was performed on five models. Three of them based on the Convolutional Neural Network (CNN) architectures; the EfficientNet-B4, ResNet50 and Xception models. The other two were Vision transformers; the ViT B-16 and ViT B-32.

The EfficientNet-B4 posted the best overall performance with a test accuracy of about 97.9%. For ease of use, the best performing model was deployed in a web application to allow the user upload the image and get results in a matter of seconds.

The achieved results show that the trained model has the capability to serve as a clinical decision support to radiologists during TB screening.