MD Ajmain Mahtab, Sanjida Tasnim, Manosh Sur Choudhury, Sefatul Wasi, Siam Tahsin Bhuiyan, Saadia Binte Alam, Rashedur Rahman
2025 22nd International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
In: 2025 22nd International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE)
IEEE, pp. 1-6
Tuberculosis remains a global health problem, particularly in resource-limited settings in which early and accurate diagnosis is paramount. This research presents SensaNet, an efficient yet light-weight binary tuberculosis (TB) classification for histopathological image patches. SensaNet is evaluated against a well-curated set of 27,987 Kinyoun-stained image patches that were scanned from digitized slides of sputum smears, with wellbalanced bacilli-positive and negative distributions. SensaNet combines current architectural innovations such as Squeeze-and-Excitation (SE) blocks, Swish activation, and single-head self-attention to amplify feature representation in channel and spatial domains with lower memory cost. Experimental results confirm that SensaNet achieves an accuracy of 98.54%, precision of 98.19%, and recall of 98.95%, outperforming several baseline architectures on sensitivity with the compact size of 12.2 MB and only above 1 million parameters. Comparative analysis proves SensaNet's suitability for TB diagnosis, offering a good trade-off between diagnostic performance and computational expense. These results weigh in favor of the model's potential deployment for real-time point-of-care diagnostics, particularly for low-resource environments.