1Research Scholar, Department of Computer Science and Engineering, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India
2Associate Professor, Department of Computer Science and Engineering, Vels Institute of Science, Technology and Advanced Studies (VISTAS), Chennai, India
Corresponding Author Email: pvhemavathi.se@vistas.ac.in
Tuberculosis (TB) remains a major global health concern, particularly in resource-limited regions where early and accurate diagnosis is crucial. This study proposes a novel deep learning-based approach for TB detection using microscopic sputum smear images. The core of the system is an enhanced Bidirectional Long Short-Term Memory (Bi-LSTM) model, tailored to capture complex sequential patterns within medical image features. To improve classification accuracy and convergence efficiency, the Firefly Algorithm is employed to optimize the model's hyperparameters. Preprocessing techniques, including noise reduction and contrast enhancement, are applied to improve image quality, followed by feature extraction using convolutional layers. The optimized Bi-LSTM model is trained and validated on a curated dataset of TB-positive and TB-negative images. Experimental results demonstrate superior performance in terms of accuracy, sensitivity, and specificity compared to conventional models. This framework highlights the potential of integrating evolutionary optimization with deep learning for robust TB diagnosis in clinical settings.
Keywords: Tuberculosis detection, Bi-LSTM, Firefly Algorithm, microscopic images, deep learning, medical image analysis, optimization, sputum smear.
How to cite this article: Hemavathi PV, Sridevi S. Microscopic Image-Based TB Detection Using an Enhanced Bi-LSTM Model Optimized by Firefly Algorithm. Int J Drug Deliv Technol. 2026;16(5s): 688-692; DOI: 10.25258/ijddt.16.5s.87
Source of support: None
Conflict of interest: None