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Chest computed tomography (CT) is an effective clinical tool for estimating the patient\u2019s severity levels and deciding appropriate treatment regimes. In this paper, we use a deep learning method for detecting COVID-19 using chest CT images with the more advanced balanced dataset. We used a dataset of 8054 real patient CT scans, of which 5427 had COVID-19 and 4223 were Non-COVID-19 patient images. Our model had an average detection accuracy of 91.96% on the test dataset. In conclusion, Automated Deep Learning (DL) methodologies allow for speedy evaluation of CT images to detect COVID-19.<\/jats:p>","DOI":"10.1007\/978-3-031-37649-8_7","type":"book-chapter","created":{"date-parts":[[2023,7,25]],"date-time":"2023-07-25T04:02:08Z","timestamp":1690257728000},"page":"67-75","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Competent Deep Learning Model to Detect COVID-19 Using Chest CT Images"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3880-0308","authenticated-orcid":false,"given":"Somenath","family":"Chakraborty","sequence":"first","affiliation":[]},{"given":"Beddhu","family":"Murali","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,7,25]]},"reference":[{"issue":"10223","key":"7_CR1","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/S0140-6736(20)30183-5","volume":"395","author":"C Huang","year":"2020","unstructured":"Huang, C., et al.: Clinical features of patients infected with 2019 novel coronavirus in Wuhan, China. 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