Abstract
Tuberculosis (TB) is a communicable disease that mainly affects lungs by a bacteria Mycobacterium Tuberculosis. It remains a major global health challenge in both developed and developing countries and identified as the leading reason for global death after Covid-19. In several countries in Africa and Asia, screening for pulmonary tuberculosis is done through Ziehl-Neelsen sputum smear images due to cost-effectiveness. Manual detection is time-consuming and strenuous work leading to misdiagnosis and low detection rate. It takes several hours to analyze a single slide to screen the patient for tuberculosis. This study focusses on proposing a novel architecture based on deep learning for mask generation and segmentation. Also, to determine suitable pre-processing techniques for sputum images. The quality of the sputum smear images captured under microscope depends on various factors, mainly the staining procedure and nature of microscope. The pre-processing techniques were analyzed in detail and an effective pre-processed image was determined using image quality metrics analysis. A novel pixel-based mask generation architecture segZnet is proposed. The preprocessed images and corresponding masks are fed to Unet architecture for segmentation. The accuracy of the proposed method is 98.5%.
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Pitchumani Angayarkanni, S., Vanitha, V., Karan, V., Sivant, M. (2022). A Novel Architecture for Improving Tuberculosis Detection from Microscopic Sputum Smear Images. In: Chen, J.IZ., Tavares, J.M.R.S., Shi, F. (eds) Third International Conference on Image Processing and Capsule Networks. ICIPCN 2022. Lecture Notes in Networks and Systems, vol 514. Springer, Cham. https://doi.org/10.1007/978-3-031-12413-6_5
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