Deep learning network selection and optimized information fusion for enhanced COVID‐19 detection

Author:

Ali Muhammad Umair1,Zafar Amad1ORCID,Tanveer Jawad2ORCID,Khan Muhammad Attique3ORCID,Kim Seong Han1,Alsulami Mashael M.4,Lee Seung Won5

Affiliation:

1. Department of Intelligent Mechatronics Engineering Sejong University Seoul Republic of Korea

2. Department of Computer Science and Engineering Sejong University Seoul Republic of Korea

3. Department of Computer Science HITEC University Taxila Taxila Pakistan

4. Department of Information Technology, College of Computers and Information Technology Taif University Taif Saudi Arabia

5. Department of Precision Medicine Sungkyunkwan University School of Medicine Suwon Republic of Korea

Abstract

AbstractThis study proposes a wrapper‐based technique to improve the classification performance of chest infection (including COVID‐19) detection using X‐rays. Deep features were extracted using pretrained deep learning models. Ten optimization techniques, including poor and rich optimization, path finder algorithm, Henry gas solubility optimization, Harris hawks optimization, atom search optimization, manta‐ray foraging optimization, equilibrium optimizer, slime mold algorithm, generalized normal distribution optimization, and marine predator algorithm, were used to determine the optimal features using a support vector machine. Moreover, a network selection technique was used to select the deep learning models. An online chest infection detection X‐ray scan dataset was used to validate the proposed approach. The results suggest that the proposed wrapper‐based automatic deep learning network selection and feature optimization framework has a high classification rate of 97.7%. The comparative analysis further validates the credibility of the framework in COVID‐19 and other chest infection classifications, suggesting that the proposed approach can help doctors in clinical practice.

Funder

National Research Foundation of Korea

Publisher

Wiley

Subject

Electrical and Electronic Engineering,Computer Vision and Pattern Recognition,Software,Electronic, Optical and Magnetic Materials

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