Journal
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
Volume 2021, Issue -, Pages -Publisher
HINDAWI LTD
DOI: 10.1155/2021/5527923
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The study suggests that transfer learning-based frameworks are a viable alternative for detecting COVID-19, achieving higher accuracy and recall rates compared to traditional methods.
This paper aims to investigate the use of transfer learning architectures in the detection of COVID-19 from CT lung scans. The study evaluates the performances of various transfer learning architectures, as well as the effects of the standard Histogram Equalization and Contrast Limited Adaptive Histogram Equalization. The findings of this study suggest that transfer learning-based frameworks are an alternative to the contemporary methods used to detect the presence of the virus in patients. The highest performing model, the VGG-19 implemented with the Contrast Limited Adaptive Histogram Equalization, on a SARS-CoV-2 dataset, achieved an accuracy and recall of 95.75% and 97.13%, respectively.
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