A novel Ziehl-Neelsen sputum smear image database and deep learning strategy for automated detection of tuberculosis bacilli.

The microscopic detection of Mycobacterium tuberculosis bacilli in Ziehl-Neelsen (ZN)-stained sputum smears remains essential for tuberculosis (TB) diagnosis, but it is limited by inter-observer variability, high operational workload, and a shortage of qualified specialists. Deep learning (DL) has shown strong potential to support this task; however, progress is constrained by the limited availability of well-annotated datasets and the lack of standardized evaluation protocols. This study presents a comprehensive investigation of DL-based object detection methods for TB bacillus identification, comprising (i) a systematic comparison of seven detectors, (ii) the introduction of a new annotated ZN-stained dataset containing 500 images, and (iii) an image-partitioning strategy designed to mitigate the performance degradation caused by heterogeneous image resolutions. Three public datasets and the proposed dataset were evaluated using cross-validation and cross-dataset experiments. Among the seven methods tested, Faster R-CNN consistently achieved the highest overall precision, sensitivity, F1-score, and average precision across datasets. The proposed partitioning strategy significantly improved detection performance, particularly for large images. Cross-dataset experiments revealed limited generalization across laboratories, underscoring the importance of standardized datasets and context-aware training. Overall, the proposed dataset and experimental findings offer practical guidance for future research toward robust, clinically deployable AI-assisted TB diagnostic workflows.