Abstract
Tuberculosis Bacteria (TB) is a bacterial infection that kills and affects millions of people annually. For the disease to be effectively treated and to be stopped from spreading further, early detection is essential. In this paper, an image processing-based deep learning approach is proposed for the detection of TB germs. The effectiveness of the suggested method is assessed using a sizable dataset of TB images, and the outcomes show that it successfully recognizes TB. With the potential to improve patient outcomes and slow the spread of the disease, the suggested method presents a promising approach for the early detection and diagnosis of TB. For the detection of TB, convolutional neural networks (CNNs) InceptionV3, DenseNet201and EfficientNetB3 have been used on a dataset of X-ray images, and the EfficientNetB3 model achieved the highest test accuracy, which is 100%.
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Rima, S.A., Zannat, M., Haque, S.S., Kawsar, A., Jennifer, S.S., Reza, A.W. (2023). Tuberculosis Bacteria Detection Using Deep Learning Techniques. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_20
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DOI: https://doi.org/10.1007/978-981-99-7093-3_20
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