Abstract
Tuberculosis (TB) is a bacterial infection in lungs that causes the greater number of deaths throughout the world than other infectious diseases. TB is an airborne disease caused by the bacteria Mycobacterium tuberculosis or tubercle Bacillus. Severe infection of this disease in lungs leads to coughing, fever, and chest pains. The existing research in the last decade is providing a guideline into TB transmission, diagnosis, and treatment. However, early detection of TB will help to efficiently decrease the severity of disease upon identification leading to reducing the mortality rate. This will in turn decrease the strain on the public health and eventually exterminate the TB. Early detection is a screening pathway resulting in identification of susceptible persons. This paper discusses the recent breakthroughs made in diagnostics and treatment of existing TB detection by studying the X-ray images for identifying dominant features for TB in early stages using predefined algorithms, software’s and image processing. In this paper, a capable method for early detection of TB using Deep Learning is proposed.
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Kale, S.P., Patil, J., Kshirsagar, A., Bendre, V. (2022). Early Lungs Tuberculosis Detection Using Deep Learning. In: Nagar, A.K., Jat, D.S., Marín-Raventós, G., Mishra, D.K. (eds) Intelligent Sustainable Systems. Lecture Notes in Networks and Systems, vol 333. Springer, Singapore. https://doi.org/10.1007/978-981-16-6309-3_29
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DOI: https://doi.org/10.1007/978-981-16-6309-3_29
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