Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

Neha Gianchandani; Aayush Jaiswal; Dilbag Singh; Vijay Kumar; Manjit Kaur

doi:10.1007/s12652-020-02669-6

Rapid COVID-19 diagnosis using ensemble deep transfer learning models from chest radiographic images

J Ambient Intell Humaniz Comput. 2023;14(5):5541-5553. doi: 10.1007/s12652-020-02669-6. Epub 2020 Nov 16.

Authors

Neha Gianchandani¹, Aayush Jaiswal¹, Dilbag Singh², Vijay Kumar³, Manjit Kaur²

Affiliations

¹ Department of Computer Science and Engineering, School of Computing and Information Technology, Manipal University Jaipur, Jaipur, Rajasthan 303007 India.
² Computer Science Engineering, School of Engineering and Applied Sciences, Bennett University, Greater Noida, 201310 India.
³ Department of Computer Science and Engineering, National Institute of Technology Hamirpur, Hamirpur, Himachal Pradesh 177005 India.

Abstract

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy.

Keywords: COVID-19; Chest X-ray; Ensemble models; SARS-CoV-2; Transfer learning.