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- W4301478844 abstract "Tuberculosis(TB) in India is the world's largest TB epidemic. TB leads to 480,000 deaths every year. Between the years 2006 and 2014, Indian economy lost US$340 Billion due to TB. This combined with the emergence of drug resistant bacteria in India makes the problem worse. The government of India has hence come up with a new strategy which requires a high-sensitivity microscopy based TB diagnosis mechanism. We propose a new Deep Neural Network based drug sensitive TB detection methodology with recall and precision of 83.78% and 67.55% respectively for bacillus detection. This method takes a microscopy image with proper zoom level as input and returns location of suspected TB germs as output. The high accuracy of our method gives it the potential to evolve into a high sensitivity system to diagnose TB when trained at scale." @default.
- W4301478844 created "2022-10-05" @default.
- W4301478844 creator A5053595276 @default.
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- W4301478844 date "2018-01-22" @default.
- W4301478844 modified "2023-10-16" @default.
- W4301478844 title "Towards Automated Tuberculosis detection using Deep Learning" @default.
- W4301478844 doi "https://doi.org/10.48550/arxiv.1801.07080" @default.
- W4301478844 hasPublicationYear "2018" @default.
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