عنوان

Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning

Number

TL7355g8v8

.Language of Text, Soundtrack etc

انگلیسی

Title Proper

Detecting Coronavirus Disease 2019 Pneumonia in Chest X-Ray Images Using Deep Learning

General Material Designation

[Thesis]

First Statement of Responsibility

Zhu, Ziqi

Subsequent Statement of Responsibility

Wu, Yingnian

Date of Publication, Distribution, etc.

2020

Body granting the degree

Wu, Yingnian

Text preceding or following the note

2020

Text of Note

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.

Kolp, Felicity Ann

Zhu, Ziqi

UCLA

Electronic name

p

[Thesis]

276903

a

Y