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Detecting COVID-19 in X-ray Images using Transfer Learning |
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PP: 1823-1829 |
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doi:10.18576/isl/110538
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Author(s) |
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Jamal Alsakran,
Loai Alnemer,
Nouh Alhindawi,
Omayya Muard,
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Abstract |
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| Accurate and speedy detection of COVID-19 is essential to curb the spread of the disease and avoid overwhelming the health care system. COVID-19 detection using X-ray images is commonly practiced at medical centers; however, it requires the intervention of medical professionals trained in diagnosing and interpreting medical imagining. In this paper, we employ deep transfer learning models to detect COVID-19 on a dataset of over 20,000 X-ray images. Our results on 5 pretrained models (VGG19, InceptionV3, MobileNetV2, DenseNet121, and ResNet101V2) show high performance of 99% without image augmentation, and 93\% when image augmentation is used.
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