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A Hybrid Deep Learning Approach for COVID-19 Diagnosis via CT and X-Ray Medical Images
1 , 2 , 3 , 1 , * 1, 3 , * 4, 5
1  Department of Studies in Computer Science, University of Mysore, Manasagangothri, Mysore 570006, India
2  DoS in Computer Science, University of Mysore
3  Department of Studies in Computer Science and Engineering, Indian Institute of Information Technology Kottayam, India
4  Department of Computer Science and Engineering, College of Software, Kyung Hee University, Suwon-si, 17104, Republic of Korea
5  Sana’a Community College, Sana’a 5695, Republic of Yemen
Academic Editor: Frank Werner

Published: 29 September 2021 by MDPI in The 1st Online Conference on Algorithms session Artificial Intelligence Algorithms
Abstract:

COVID-19 pandemic is a global health problem since December 2019. Up to date, the total number of confirmed, recovered and deaths has exponentially increased on daily basis worldwide. In this paper, a hybrid deep learning approach is proposed to directly classify the COVID-19 disease from both chest X-ray (CXR) and CT images. Two AI-based deep learning models, namely ResNet50 and EfficientNetB0, are adopted and trained using both chest X-ray and CT images. The public datasets consist of 7,863 and 2,613 chest X-ray and CT images are respectively used to deploy, train, and evaluate the proposed deep learning models. The deep learning model of EfficientNet always performed a better classification result achieving overall diagnosis accuracies of 99.36% and 99.23% using CXR and CT images, respectively. For the hybrid AI-based model, the overall classification accuracy of 99.58% is achieved. The proposed hybrid deep learning system seems to be trust worth and reliable for assisting health care systems, patients, and physicians.

Keywords: Covid-19;Deep learning;X-ray;Computed tomography;
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