Detection of COVID‐19 from CT Lung Scans Using Transfer Learning
Sahil Ramkaran Lawton, Serestina Viriri
Abstract
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.
Topics & Concepts
Coronavirus disease 2019 (COVID-19)Transfer of learning2019-20 coronavirus outbreakSevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)Computer scienceLungArtificial intelligenceRadiologyMedicinePathologyInternal medicineDiseaseOutbreakInfectious disease (medical specialty)COVID-19 diagnosis using AIRadiomics and Machine Learning in Medical ImagingLung Cancer Diagnosis and Treatment