Automated Detection and Grading of Tuberculosis Bacilli in Ziehl Neelsen-Stained Sputum Using YOLO with IUATLD-Based Classification

Abstract

Tuberculosis (TB) remains one of the most pressing global health challenges, particularly in low and middle-income countries, where diagnostic capacity is often limited. Accurate and efficient detection of Mycobacterium tuberculosis bacilli in sputum smear samples stained with Ziehl-Neelsen remains the cornerstone of TB diagnosis. However, conventional microscopic examination is inherently labour-intensive, subject to interobserver variability and prone to human error, leading to inconsistent diagnostic outcomes. Addressing these limitations, this study proposes the development of an automated bacilli detection and quantification system utilizing the YOLO (You Only Look Once) object detection framework, specifically the YOLOv8 architecture, to improve diagnostic accuracy, consistency, and efficiency in TB identification. The research methodology encompasses image acquisition of Ziehl Neelsen-stained sputum samples from the Microbiology Laboratory of Universitas Airlangga Hospital (RSUA) and publicly available repositories, followed by meticulous annotation using Roboflow. The annotated dataset was employed to train the YOLOv8 model, and performance was evaluated through key metrics, including accuracy, precision, and error rate. The developed model achieved an overall accuracy of 73.33%, with class-wise accuracies of 100% for BTA 1+, 80% for BTA 2+, and 40% for BTA 3+ categories, conforming to IUATLD classification standards. The suboptimal performance observed in the BTA 3+ category was attributed to discrepancies in Field of View (FOV) alignment between the microscope’s ocular lens and the attached digital camera, affecting image consistency. Despite this limitation, the results demonstrate the potential of YOLO-based automated detection systems to reduce dependence on manual analysis, enhance diagnostic objectivity, and accelerate TB screening workflows. Future work should prioritize hardware calibration, particularly FOV synchronization, and dataset diversification to further refine model performance and clinical applicability. The proposed approach represents a significant step towards scalable, rapid, and reliable TB diagnosis, with implications for broader adoption in resource-constrained healthcare environments.