Detecting Covid-19 in Chest X-Rays using Transfer Learning with VGG16
Conference proceedings article
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Publication Details
Author list: Chen, Amy; Jaegerman, Jonathan; Matic, Dunja; Inayatali, Hassaan; Charoenkitkarn, Nipon; Chan, Jonathan;
Publisher: Hindawi
Publication year: 2020
Start page: 93
End page: 96
Number of pages: 4
ISBN: 9781450388238
ISSN: 0146-9428
eISSN: 1745-4557
Languages: English-Great Britain (EN-GB)
Abstract
Covid-19 is a novel epidemic that has hugely impacted countries worldwide [13]; and for which there is a need for quick and accurate screening methods. Current testing methods include the reverse transcription-polymerase chain reaction test and medical diagnosis using computed tomography scans. Both of these require expensive technologies as well as highly-trained practitioners and thus are in short supply [18]. Less developed countries and overloaded hospitals have increased the demand for cheap, easy and accurate screening methods [4]. X-ray devices are now cheap, portable and easy to use; there are few professionals, however, who are capable of manually identifying Covid-19 from a chest X-ray. We suggest implementing a machine learning model that incorporates transfer learning to automatically detect Covid-19 from chest X-ray images. The suggested model is built on top of the VGG16 architecture and pre-trained ImageNet weights. Compared with the VGG19, Inception-V3, Inception-ResNet, Xception, RestNet152-V2, and DenseNet201 models, the VGG16 model achieved the highest testing accuracy of 98% on 10 epochs as well as high positive-class accuracy. Gradient-weighted class activation mapping (Grad-CAM) was also applied to detect the regions that have a greater impact on the model classification decision. © 2020 ACM.
Keywords
Chest X-Rays, VGG16
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