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

被引：0
作者：
Neha Gianchandani
Aayush Jaiswal
Dilbag Singh
Vijay Kumar
Manjit Kaur
机构：
[1] Manipal University Jaipur,Department of Computer Science and Engineering, School of Computing and Information Technology
[2] Bennett University,Computer Science Engineering, School of Engineering and Applied Sciences
[3] National Institute of Technology Hamirpur,Department of Computer Science and Engineering
来源：
Journal of Ambient Intelligence and Humanized Computing | 2023年 / 14卷
关键词：
COVID-19; SARS-CoV-2; Transfer learning; Chest X-ray; Ensemble models;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
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.
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页码：5541 / 5553
页数：12
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