Abstract. Tuberculosis remains one of the most widespread infectious diseases, requiring timely and accurate diagnosis. In this regard, this study presents the development and comparative evaluation of deep learning models for the automatic detection of tuberculosis in chest X-ray images. The aim of the study is to analyze the effectiveness of YOLOv8 and RT-DETR model families for detecting pathological changes and to determine the most accurate and robust architecture. The initial dataset consisting of 2441 images was expanded to 5859 using data augmentation techniques and annotated with bounding boxes in the Roboflow environment.
Within the study, YOLOv8 and RT-DETR models were trained and comparatively analyzed. Model performance was evaluated using Precision, Recall, F1-score, and mAP metrics. Experimental results showed that the YOLOv8m model achieved the best performance (Precision = 0.94, Recall = 0.91, mAP50= 0.95, mAP50-95 = 0.72), providing an optimal balance between accuracy and computational efficiency. Based on this model, an intelligent agent for automatic analysis of medical images was developed. The obtained results confirm the effectiveness of YOLO-based models for computer-aided diagnosis and clinical decision support systems.

Keywords: tuberculosis, chest X-ray, deep learning, YOLO, RT-DETR, object detection, medical images, AI agent.