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• W4281736156 endingPage "100041" @default.
• W4281736156 startingPage "100041" @default.
• W4281736156 abstract "Over the past year, the AI community has constructed several deep learning models for diagnosing COVID-19 based on the visual features of chest X-rays. While deep learning researchers have commonly focused much of their attention on designing deep learning classifiers, only a fraction of these same researchers have dedicated effort to including a segmentation module in their system. This is unfortunate since other applications in radiology typically require segmentation as a necessary prerequisite step in building truly deployable clinical models. Differentiating COVID-19 from other pulmonary diseases can be challenging as various lung diseases share common visual features with COVID-19. To help clarify the diagnosis of suspected COVID-19 patients, we have designed our deep learning pipeline with a segmentation module and ensemble classifier. Following a detailed description of our deep learning pipeline, we present the strengths and shortcomings of our approach and compare our model with other similarly constructed models. While doing so, we focus our attention on widely circulated public datasets and describe several fallacies we have noticed in the literature concerning them. After performing a thorough comparative analysis, we demonstrate that our best model can successfully obtain an accuracy of 91 percent and sensitivity of 92 percent." @default.
• W4281736156 created "2022-06-13" @default.
• W4281736156 creator A5008218083 @default.
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• W4281736156 date "2022-06-01" @default.
• W4281736156 modified "2023-09-27" @default.
• W4281736156 title "A deep learning segmentation-classification pipeline for X-ray-based COVID-19 diagnosis" @default.
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• W4281736156 doi "https://doi.org/10.1016/j.bea.2022.100041" @default.
• W4281736156 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35663366" @default.
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