Explainable Hybrid Deep Learning for Tuberculosis Detection in Resource-Constrained Settings

Tuberculosis (TB) remains a significant global public health challenge, particularly in resource-constrained settings where limited access to specialized diagnostic services may delay disease detection. This study developed and comparatively evaluated an automated and explainable framework for TB detection from chest radiographs using conventional machine learning, deep learning, and hybrid learning approaches. A publicly available Kaggle dataset comprising 4,200 chest X-ray images, including 3,500 normal and 700 TB-positive cases, was used for model development and evaluation. The images were resized, normalized, augmented, and balanced to improve training consistency and address class imbalance. Conventional classifiers were trained using Histogram of Oriented Gradients (HOG) features, while seven deep learning architectures and multiple CNN-based hybrid models were evaluated using 70:30 and 80:20 train–test splits under different augmentation and optimization settings. Among the conventional classifiers, SVM achieved a maximum accuracy of 99.50\% with data augmentation. VGG16 achieved the highest overall deep-learning accuracy of 99.88% under the 80:20 non-augmented configuration, whereas DenseNet201 achieved 99.86% with data augmentation. Among the hybrid approaches, VGG16 combined with Logistic Regression achieved the highest accuracy of 99.76\%. Gradient-weighted Class Activation Mapping (Grad-CAM) was further employed to enhance model interpretability by highlighting image regions that contributed to classification decisions. The findings demonstrate that deep learning and hybrid approaches provide highly competitive performance for automated TB screening, while Grad-CAM enhances the interpretability and transparency of model predictions. However, external multicenter validation is required before clinical deployment.