Improving Smear-Negative Tuberculosis Detection Using Data Augmentation and Faster R-CNN

Abstract

Smear-Negative Pulmonary Tuberculosis (SNPT) remains a major diagnostic challenge due to the low bacterial load that frequently causes false negative results in sputum microscopy. Chest X-ray imaging is commonly used as a complementary diagnostic tool however its interpretation relies heavily on expert radiologists and is prone to subjectivity. Recent developments in deep learning particularly object detection models provide promising opportunities to improve diagnostic accuracy. This study aims to develop and evaluate a deep learning based approach for SNPT detection in chest X-ray images using a Faster R-CNN model with a ResNet architecture. The proposed method applies data augmentation techniques including flipping rotation scaling and random brightness adjustment to enhance training data diversity and reduce overfitting. The model was implemented using PyTorch and evaluated using accuracy precision recall and F1 score. Experimental results indicate that data augmentation substantially improves performance achieving 76.60% accuracy and 68.57% F1 score compared to 53.06% accuracy and 51.06% F1 score without augmentation. Improved recall reflects higher sensitivity in detecting SNPT cases. These findings indicate that data augmentation enhances the robustness and generalization of Faster R-CNN models for SNPT detection and supports the potential of AI assisted diagnostic systems in tuberculosis screening programs.

Keywords

• Data Augmentation
• SNPT
• Faster R-CNN
• ResNet Architecture
• Chest X-ray Imaging