Detection of COVID‐19 from CT Lung Scans Using Transfer Learning | Zendy

Sahil Ramkaran Lawton | Zendy; Serestina Viriri | Zendy

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Detection of COVID‐19 from CT Lung Scans Using Transfer Learning

Author(s) -

Sahil Ramkaran Lawton,

Serestina Viriri

Publication year - 2021

Publication title -

computational intelligence and neuroscience

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.605

H-Index - 52

eISSN - 1687-5273

pISSN - 1687-5265

DOI - 10.1155/2021/5527923

Subject(s) - covid-19 , transfer of learning , computer science , lung , artificial intelligence , radiology , medicine , pathology , disease , infectious disease (medical specialty) , outbreak

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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