Open Access

ARTICLE

A Classification–Detection Approach of COVID-19 Based on Chest X-ray and CT by Using Keras Pre-Trained Deep Learning Models

by Xing Deng, Haijian Shao, Liang Shi, Xia Wang, Tongling Xie

1 School of Computer Science, Jiangsu University of Science and Technology, Zhenjiang, 212003, China
2 School of Automation, Key Laboratory of Measurement and Control for CSE, Ministry of Education, Southeast University, Nanjing, 210096, China
3 School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, 212003, China
4 School of Information Science and Technology, Nantong University, Nantong, 226019, China
5 Department of Electrical and Computer Engineering, National University of Singapore, 117583, Singapore
6 The People’s Hospital of RUGAO, Nantong, 226500, China

* Corresponding Author: Haijian Shao. Email:

Computer Modeling in Engineering & Sciences 2020, 125(2), 579-596. https://doi.org/10.32604/cmes.2020.011920

Received 05 June 2020; Accepted 07 September 2020; Issue published 12 October 2020

Abstract

The Coronavirus Disease 2019 (COVID-19) is wreaking havoc around the world, bring out that the enormous pressure on national health and medical staff systems. One of the most effective and critical steps in the fight against COVID-19, is to examine the patient’s lungs based on the Chest X-ray and CT generated by radiation imaging. In this paper, five keras-related deep learning models: ResNet50, InceptionResNetV2, Xception, transfer learning and pre-trained VGGNet16 is applied to formulate an classification–detection approaches of COVID-19. Two benchmark methods SVM (Support Vector Machine), CNN (Convolutional Neural Networks) are provided to compare with the classification–detection approaches based on the performance indicators, i.e., precision, recall, F1 scores, confusion matrix, classification accuracy and three types of AUC (Area Under Curve). The highest classification accuracy derived by classification–detection based on 5857 Chest X-rays and 767 Chest CTs are respectively 84% and 75%, which shows that the keras-related deep learning approaches facilitate accurate and effective COVID-19-assisted detection.

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Citations