Md Ajmain Mahtab
- Research Assistant, CCDS
Bachelors
Department of Computer Science and Engineering
BRAC University, Bangladesh
Conference Paper2025Cited by 1
SensaNet: a lightweight DL model for tuberculosis detection in histopathological images
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)
IEEE, pp. 1-6
Abstract
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.
Keywords
- tuberculosis detection
- Swish activation
- resource-limited settings
- sputum smear microscopy
- point-of-care testing
- self-attention
- Kinyoun staining
- Binary classification
- lightweight deep learning
- squeeze-and-excitation
CCDS Authors
Manosh Sur Choudhury
- Research Assistant, CCDS
Masters
University of Dhaka, Bangladesh
Bachelors
Military Institute of Science and Technology, Bangladesh
Siam Tahsin Bhuiyan
- Intern, CCDS
Bachelors
Independent University, Bangladesh (IUB)
Published at
Scientific Reports
Saadia Binte Alam, PhD
- Director, MIRA & WiSE Wings
Associate Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)
Published at
HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications
Md Rashedur Rahman, D. Eng.
- Co-Director, MIRA & NEST Wings
Assistant Professor
Department of Computer Science and Engineering
Independent University, Bangladesh (IUB)
Published at
CHI, HCII, Scientific Reports, Cognitive Computation, Neural Computing and Applications
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Cited By
- 1.Sayeda Subha Ashrafi, Tushar Ahmed Shishir. (2026). Deep Learning-Based Automated Breast Cancer Detection from Histopathology Images: Benchmarking CNN and Transformer Architectures. , 1–6[10.1109/peccii70991.2026.11662018]