TBNet: A Chest X-Ray Classifier Supporting Image Segmentation With Self-Attention Mechanism for Secondary Tuberculosis Detection

Abstract

Several studies have been conducted to develop tuberculosis detection using artificial intelligence-based chest radiographs (CXR). However, CXR images also contain atypical information on pulmonary tuberculosis that could affect the accuracy of the learning. This work aims to identify the regions of interest (RoI) in CXR images that support information on secondary pulmonary tuberculosis in the upper lung area and train a vision transformer network to build a robust CXR classifier. The dataset used in this study consists of 1,304 annotated CXR images. The ground-truth masks of the upper one-third lung region were manually created using ImageJ. The methods include enhancing the CXR image using Contrast-limited Adaptive Histogram Equalization before extracting RoI using the Attention U-Net segmentation algorithm. The informative CXR images were then fed into the self-attention network of the vision transformer (ViT-L/16). The results show that the segmentation model achieves 100% accuracy in identifying important areas on images that have undergone image contrast pre-processing. The classifier model exhibits stable performance during training, achieving an accuracy of 97.75%. The test results on 260 images, 130 normal and 130 TB, show that TBNet achieves 97.04% accuracy, 97.96% sensitivity, 96.92% specificity, and 96.94% precision. TBNet achieves a high accuracy rate and has a size of 127 MB for segmentation and 1.12 GB for classification; thus, practically, the proposed TBNet can be deployed to support tuberculosis detection system. These findings contribute to research on medical image analysis by highlighting the importance of region-specific annotation and transformer-based architectures for secondary tuberculosis detection.