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The base models used for detection and classification were AlexNet, Visual Geometry Group Network with 16 layers, residual network, DensNet, GoogLeNet, MobileNet, Inception, and extreme Inception. U-Net and voxel-based broad learning network were used for segmentation. Even with limited datasets, these methods proved to be beneficial for efficiently identifying the occurrence of COVID-19. To further validate these observations, we conducted an experimental study using a simple CNN framework for the binary classification of COVID-19 CT images. We achieved an accuracy of 93% with an F1-score of 0.93. Thus, with the availability of improved medical image datasets, it is evident that CNNs are very useful for the efficient diagnosis and prognosis of COVID-19.<\/jats:p>","DOI":"10.1186\/s42492-021-00078-w","type":"journal-article","created":{"date-parts":[[2021,5,5]],"date-time":"2021-05-05T12:03:45Z","timestamp":1620216225000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":34,"title":["Convolutional neural networks for the diagnosis and prognosis of the coronavirus disease pandemic"],"prefix":"10.1186","volume":"4","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9715-0587","authenticated-orcid":false,"given":"Sneha","family":"Kugunavar","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"C. 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