EfficientNet-based detection transformer with explainable AI for tuberculosis bacilli detection and severity grading

TB continues to be a serious health issue in the world, with about 10 million people infected each year. Good quality tools that can help in making sound medical decisions are essential for improving the health status of patients. This paper explores an automatic TB bacilli detection and grading system based on artificial intelligence. The detection system is based on the Real-Time Detection Transformer (RT-DETR), allowing for accurate and effective bacilli identification at different scales. To improve model transparency and support clinician interpretability, Gradient-weighted Class Activation Mapping (Grad-CAM) was incorporated. The system incorporates an IUATLD-based grading scheme, endorsed by the World Health Organization (WHO), classifying the severity of infection into three levels: TB 1+, TB 2+, and TB 3+. The grades are based on bacilli counts seen on 300 slides or Fields of View (FoV), providing a standardized and objective measurement. The proposed RT-DETR with an EfficientNet-B0 backbone achieved a detection performance of 92.5% mAP@50, 84.8% recall, and 82.8% box precision on the TB Phone Camera dataset, indicating strong detection performance compared with previously reported YOLO-based approaches while preserving real-time inference performance. The proposed model achieved a severity grading accuracy of 94.45%. By integrating Explainable Artificial Intelligence (XAI) with established IUATLD grading criteria, the proposed framework provides an interpretable approach to automated TB detection and severity grading.