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- W4285184258 abstract "AbstractTuberculosis is a major health threat in many regions of the world. Opportunistic infections in immunocompromised HIV/AIDS patients and multi-drug-resistant bacterial strains have exacerbated the problem, while diagnosing tuberculosis remains challenging. Medical images have made a high impact on medicine, diagnosis, and treatment. The most important part of image processing is image segmentation. This chapter presents a novel X-ray of lungs segmentation method using the U-net model. First, we construct the U-net which combine the lungs and mask. Then, we convert to problem of positive and negative TB lungs into the segmentation of lungs, and extract the lungs by subtracting the chest from the radiography. In experiment, the proposed model achieves 97.62% on the public dataset of collection by Shenzhen Hospital, China and Montgomery County X-ray Set.KeywordsComputer-aided detection and diagnosisLungSegmentationTuberculosis (TB)X-ray imaging" @default.
- W4285184258 created "2022-07-14" @default.
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- W4285184258 date "2022-01-01" @default.
- W4285184258 modified "2023-10-17" @default.
- W4285184258 title "A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network" @default.
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- W4285184258 doi "https://doi.org/10.1007/978-981-19-2057-8_9" @default.
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