An Effective Deep Learning Model for COVID-19 Detection from Chest X-Ray

Simon Emmanuel Ikoojo, Deme Chuwang Abraham and Nentawe Gurumdimma
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Simon Emmanuel Ikoojo: Department of Computer Science, University of Jos, Plateau State, Nigeria
Deme Chuwang Abraham: Department of Computer Science, University of Jos, Plateau State, Nigeria
Nentawe Gurumdimma: Department of Computer Science, University of Jos, Plateau State, Nigeria

International Journal of Research and Innovation in Applied Science, 2021, vol. 6, issue 11, 55-59

Abstract: A new viral disease that easily spreads was in December 2019 discovered in Wuhan city in China and was named by the World Health Organization (WHO) as COVID-19. The symptoms can be either mild or severe and mostly in older people who have hypertension, diabetes, and heart or lung disease. Early screening has proved to be effective in reducing the spread and the RT-PCR test is been employed for testing which is expensive and time-consuming. Deep learning using CNN on Chest X-rays can be used to detect the infection. In this paper, three deep learning models (VGG16, Xception, and InceptionV3) were proposed for detecting COVID-19. These models were pretrained using images from ImageNet with the proposed Inception model achieving the highest accuracy of 98.28%. The f1-score for Xception, VGG, and Inception approaches are 98%, 95%, and 95% respectively. The proposed approaches achieved a precision score of 100%, 100%, and 96% in classifying COVID-19 cases for Inception, Xception, and VGG16 respectively.

Date: 2021
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