Classification of Tuberculosis Bacilli from Microscopic Sputum Smear Images Using Swin Transformer

Tuberculosis (TB) has been a great challenge in the health world, and proper treatment requires proper diagnosis at the right time. This paper has classified the bacilli in sputum samples into single/simple and clump formations using deep-learning transformer techniques. The method starts with semantic segmentation, where TransUNet model has been used on sputum images to detect regions containing the bacilli. The superior visual task performer, the Swin Transformer, further classifies the regions identified. Accuracy, precision, recall, F1-score, and AUC-ROC are the metrics used to evaluate the model's performance. This classification contributes to TB diagnosis by quantifying bacterial load and categorizing infection severity. This type of differentiation may guide clinicians in developing more precise treatment strategies as the variability of infection severity may be observed in patients suffering from TB. Integrating the recent transformer models, such as Swin Transformer and TransUNet, into the microbiological diagnosis process will improve the speed of TB diagnosis, and automated bacterial classification will occur through an organized process. This study will help diagnose TB by improving the capacity of diagnosis, which subsequently helps in better decision-making regarding treatment and management, independent of human interpretation. The successful classification of TB bacilli resulted in an average accuracy of 93%, average precision of 93%, average recall of 92.5%, and average F1-score of 92.5%.