Deep learning models for tuberculosis detection and infected region visualization in chest X-ray images

Objective

Tuberculosis (TB) is among the most frequent causes of infectious-disease-related mortality. Despite being treatable by antibiotics, tuberculosis often goes misdiagnosed and untreated, especially in rural and low-resource areas. Chest X-rays are frequently used to aid diagnosis; however, this presents additional challenges because of the possibility of abnormal radiological appearance and a lack of radiologists in areas where the infection is most prevalent. Implementing deep-learning-based imaging techniques for computer-aided diagnosis has the potential to enable accurate diagnoses and lessen the burden on medical specialists. In the present work, we aimed to develop deep-learning-based segmentation and classification models for accurate and precise detection of tuberculosis in chest X-ray images, with visualization of infection using gradient-weighted class activation mapping (Grad-CAM) heatmaps.

Methods

First, we trained the UNet segmentation model using 704 chest X-ray radiographs taken from the Montgomery County and Shenzhen Hospital datasets. Next, we implemented the trained UNet model on 1,400 tuberculosis and control chest X-ray scans to segment the lung region. The images were taken from the National Institute of Allergy and Infectious Diseases (NIAID) TB portal program dataset. Then, we applied the deep learning Xception model to classify the segmented lung region into tuberculosis and normal classes. We further investigated the visualization capabilities of the model using Grad-CAM to view tuberculosis abnormalities in chest X-rays and discuss them from radiological perspectives.

Results

For segmentation by the UNet model, we achieved accuracy, Jaccard index, Dice coefficient, and area under the curve (AUC) values of 96.35%, 90.38%, 94.88%, and 0.99, respectively. For classification by the Xception model, we achieved classification accuracy, precision, recall, F1-score, and AUC values of 99.29%, 99.30%, 99.29%, 99.29%, and 0.999, respectively. The Grad-CAM heatmap images from the tuberculosis class showed similar heatmap patterns, where lesions were primarily present in the upper part of the lungs.

Conclusion

The findings may verify our system's efficacy and superiority to clinician precision in tuberculosis diagnosis using chest X-rays and raise the possibility of a valuable setup, particularly in environments with a scarcity of radiological expertise.