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COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images

A. S. Al-Waisy1, Mazin Abed Mohammed1, Shumoos Al-Fahdawi1, M. S. Maashi2, Begonya Garcia-Zapirain3, Karrar Hameed Abdulkareem4, S. A. Mostafa5, Nallapaneni Manoj Kumar6, Dac-Nhuong Le7,8,*

1 College of Computer Science and Information Technology, University of Anbar, Anbar, 31001, Iraq
2 College of Computer and Information Sciences, King Saud University, Riyadh, 11451, Saudi Arabia
3 eVIDA Lab, University of Deusto. Avda/Universidades, Bilbao, 24.48007, Spain
4 College of Agriculture, Al-Muthanna University, Samawah, 66001, Iraq
5 Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia, Johor, 86400, Malaysia
6 School of Energy and Environment, City University of Hong Kong, Kowloon, 83, Hong Kong
7 Institute of Research and Development, Duy Tan University, Danang, 550000, Vietnam
8 Faculty of Information Technology, Duy Tan University, Danang, 550000, Vietnam

* Corresponding Author: Dac-Nhuong Le. Email: email

(This article belongs to the Special Issue: Machine Learning and Computational Methods for COVID-19 Disease Detection and Prediction)

Computers, Materials & Continua 2021, 67(2), 2409-2429. https://doi.org/10.32604/cmc.2021.012955

Abstract

Coronavirus (COVID-19) epidemic outbreak has devastating effects on daily lives and healthcare systems worldwide. This newly recognized virus is highly transmissible, and no clinically approved vaccine or antiviral medicine is currently available. Early diagnosis of infected patients through effective screening is needed to control the rapid spread of this virus. Chest radiography imaging is an effective diagnosis tool for COVID-19 virus and follow-up. Here, a novel hybrid multimodal deep learning system for identifying COVID-19 virus in chest X-ray (CX-R) images is developed and termed as the COVID-DeepNet system to aid expert radiologists in rapid and accurate image interpretation. First, Contrast-Limited Adaptive Histogram Equalization (CLAHE) and Butterworth bandpass filter were applied to enhance the contrast and eliminate the noise in CX-R images, respectively. Results from two different deep learning approaches based on the incorporation of a deep belief network and a convolutional deep belief network trained from scratch using a large-scale dataset were then fused. Parallel architecture, which provides radiologists a high degree of confidence to distinguish healthy and COVID-19 infected people, was considered. The proposed COVID-DeepNet system can correctly and accurately diagnose patients with COVID-19 with a detection accuracy rate of 99.93%, sensitivity of 99.90%, specificity of 100%, precision of 100%, F1-score of 99.93%, MSE of 0.021%, and RMSE of 0.016% in a large-scale dataset. This system shows efficiency and accuracy and can be used in a real clinical center for the early diagnosis of COVID-19 virus and treatment follow-up with less than 3 s per image to make the final decision.

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APA Style
Al-Waisy, A.S., Mohammed, M.A., Al-Fahdawi, S., Maashi, M.S., Garcia-Zapirain, B. et al. (2021). Covid-deepnet: hybrid multimodal deep learning system for improving COVID-19 pneumonia detection in chest x-ray images. Computers, Materials & Continua, 67(2), 2409-2429. https://doi.org/10.32604/cmc.2021.012955
Vancouver Style
Al-Waisy AS, Mohammed MA, Al-Fahdawi S, Maashi MS, Garcia-Zapirain B, Abdulkareem KH, et al. Covid-deepnet: hybrid multimodal deep learning system for improving COVID-19 pneumonia detection in chest x-ray images. Comput Mater Contin. 2021;67(2):2409-2429 https://doi.org/10.32604/cmc.2021.012955
IEEE Style
A.S. Al-Waisy et al., "COVID-DeepNet: Hybrid Multimodal Deep Learning System for Improving COVID-19 Pneumonia Detection in Chest X-ray Images," Comput. Mater. Contin., vol. 67, no. 2, pp. 2409-2429. 2021. https://doi.org/10.32604/cmc.2021.012955

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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