Abstract
Accurate identification of Mycobacterium tuberculosis (M. tuberculosis) is a critical step in the diagnosis of tuberculosis. Existing object detection methods struggle with the challenges posed by the varied morphology and size of M. tuberculosis in sputum smear images, which makes precise targeting difficult. To solve these problems, an improved YOLOv8s model is proposed. Specifically, an additional detection head is added to focus on small target information. Second, a multi-scale feature fusion module is introduced to adapt the model to different sizes of M. tuberculosis. In addition, a convolutional layer is added to the Coordinate Attention (CA) module to extract more advanced semantic features. Finally, a self-attention mechanism is added after the CA module to enhance the model's ability to accurately understand and localize the varied morphology of M. tuberculosis. Our model performed well with an average precision of 85.7 % when tested on a publicly available dataset. This clearly demonstrates the effectiveness of our proposed model in M. tuberculosis detection.