OPTIMIZED YOLOv9-PES ARCHITECTURE FOR ENHANCED MICROSCOPIC DETECTION OF MYCOBACTERIUM TUBERCULOSIS

Tuberculosis remains a leading cause of infectious disease mortality globally, with delayed diagnosis contributing to transmission chains and drug-resistant strains. Automating detection is thus a critical public health priority. This approach significantly reduces the likelihood of false positives or missed detections in specific scenarios. Our study focuses on high-precision detection of Mycobacterium tuberculosis in microscopic images. By optimizing the YOLO architecture, we achieved efficient recognition of microscale Mycobacterium tuberculosis targets against complex microscopic backgrounds. We adopted the latest YOLOv9 model as the foundational framework and made a series of targeted enhancements. In comparative experiments using the same dataset, our improved model achieved a mean average precision (mAP) of 89.2%, demonstrating superior accuracy compared to similar microscopic image recognition algorithms. The experimental results highlight the advantages of the YOLO model in microscopic image recognition tasks, particularly in dealing with complex backgrounds and detecting small targets. These advancements hold significant implications for both clinical workflows and global tuberculosis eradication efforts.