ResisTrack: A CNN-Based System for Binary and Multiclass Classification of Drug-Resistant Tuberculosis Using Chest X-Ray Images

Abstract

This study presents the development and evaluation of ResisTrack, a deep learningbased web diagnostic system for classifying drug-resistant tuberculosis (TB) using chest X-ray (CXR) images. The classification targets three clinically relevant TB categories: drug-sensitive (DS-TB), multi-drug-resistant (MDR non-XDR), and extensively drug-resistant (XDR-TB). A U-Net model was first trained on Montgomery and Shenzhen datasets for lung segmentation, followed by preprocessing steps to normalize and resize the images. Using the TB Portals dataset, two classification tasks were formulated: binary (DS-TB vs. MDR non-XDR) and multiclass (DS-TB, MDR non-XDR, XDR-TB). To address class imbalance, random oversampling and geometric data augmentation were applied. Transfer learning was employed with three pre-trained convolutional neural networks—DenseNet201, DenseNet121, and InceptionV3—evaluated individually and as an ensemble using hard voting. The models were tested across four experimental configurations (with and without augmentation in both binary and multiclass setups) and assessed using accuracy, AUC, precision, recall, F1, and F2 scores. The ensemble model for binary classification without augmentation achieved the best overall performance: 76.70% accuracy, 83.40% AUC, 83.98% recall, and 82.19% F2 score, highlighting its capability to minimize false negatives—critical in TB triage. This best-performing model was deployed in the ResisTrack web system, enabling real-time CXR classification with PDF reporting via a user-friendly interface. This work demonstrates the potential of deep learning to support TB resistance detection in clinical and resource-constrained settings.