Purpose:
Tuberculosis (TB) continues to be a major cause of death from infectious diseases globally. TB is treatable with antibiotics, but it is often misdiagnosed or left untreated, particularly in rural and resource-constrained regions. While chest X-rays are a key tool in TB diagnosis, their effectiveness is hindered by the variability in radiological presentations and the lack of trained radiologists in high-prevalence areas. Deep learning-based imaging techniques offer a promising approach to computer-aided diagnosis for TB, enabling precise and timely detection while alle­viating the burden on healthcare professionals. This study aims to enhance TB detection in chest X-ray images by developing deep learning models. We have observed upper and lower lobe consolidation, pleural effusion, calcification, cavity formation and military nodules. A proposed preprocessing technique has been also introduced in our work based on gamma correction and gradient based technique for contrast enhancement. We leverage the Res-UNet architecture for image segmentation and introduce a novel deep learning network for classification, targeting improved accuracy and precision in diagnostic performance.

Material and methods:
A Res-UNet segmentation model was trained using 704 chest X-ray images sourced from the Montgomery County and Shenzhen Hospital datasets. Following training, the model was applied to segment lung regions in 1400 chest X-ray scans, encompassing both TB cases and normal controls, obtained from the National Institute of Allergy and Infectious Diseases (NIAID) TB Portal program dataset. The segmented lung regions were subsequently classified as either TB or normal using a deep learning model. A gradient based technique was used for contrast enhancement by capturing intensity changes in image by comparing each pixel with its neighbour with pyramid reduction unique mapping and histogram matching along with gamma correction is used. This integrated approach of segmentation and classification aims to enhance the accuracy and precision of TB detection in chest X-ray images. Classification of segmented images was done using customised convolutional neural network, and visualisation was done using Grad-CAM.

Results:
The Res-UNet model demonstrated excellent performance for segmentation, achieving an accuracy of 98.18%, recall of 98.40%, precision of 97.45%, F1-score of 97.97%, Dice coefficient of 96.33%, and Jaccard index of 96.05%. Similarly, the classification model exhibited outstanding results, with a classification accuracy of 99.45%, precision of 99.29%, recall of 99.29%, F1-score of 99.29%, and an AUC of 99.9%. Enhanced gradient based method showed ambe of 16.51, entropy of 6.7370, CII of 86.80, psnr of 28.71, ssim of 86.83 which are quite satisfactory.

Conclusions:
The findings demonstrate the efficiency of our system in diagnosing TB from chest X-rays, potentially surpassing clinician-level precision. This underscores its effectiveness as a diagnostic tool, particularly in resourcelimited settings with restricted access to radiological expertise. Additionally, the modified Res-UNet model demonstrated superior performance compared to the standard U-Net, highlighting its potential for achieving greater dia­gnostic accuracy.