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" Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning "
Zhu, Ziqi
Wu, Yingnian
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Latin Dissertation
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English
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| Record Number
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897400
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TL7355g8v8
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| Main Entry
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Kolp, Felicity Ann
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| Title & Author
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Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning\ Zhu, ZiqiWu, Yingnian
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| Date
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2020
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2020
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| Abstract
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The coronavirus disease 2019 (COVID-19) pandemic has already become a global threat. To fight against COVID-19, effective and fast screening methods are needed. This study focuses on leveraging deep learning techniques to automatically detect COVID‐19 pneumonia in chest X-ray images. Two models are trained based on transfer learning and residual neural network. The first one is a binary classifier that separates COVID-19 pneumonia and non-COVID-19 cases. It classifies all test cases correctly. The second one is a four-class classifier that distinguishes COVID-19 pneumonia, viral pneumonia, bacterial pneumonia and normal cases. It reaches an average accuracy, precision, sensitivity, specificity, and F1-score of 93\%, 93\%, 93\%, 97\%, and 93\%, respectively. To understand on how the four-class classifier detects COVID-19 pneumonia, we apply Gradient-weighted Class Activation Mapping (Grad-CAM) method and find out that the classifier is able to focus on the patchy areas in chest X-ray images and make accurate predictions.
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Zhu, Ziqi
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UCLA
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