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Article

COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting

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Robotics and Internet-of-Things Unit (RIoTU) Lab, Prince Sultan University, Riyadh 12435, Saudi Arabia
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CISTER, INESC-TEC, ISEP, Polytechnic Institute of Porto, 4200-465 Porto, Portugal
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SEICT Lab, LR18ES44, Enicarthage, University of Carthage, Tunis 1054, Tunisia
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Faculty of Engineering, University of Moncton, Moncton, NB E1A 3E9, Canada
*
Author to whom correspondence should be addressed.
Academic Editor: Anton Civit
Appl. Sci. 2021, 11(6), 2884; https://doi.org/10.3390/app11062884
Received: 14 February 2021 / Revised: 11 March 2021 / Accepted: 17 March 2021 / Published: 23 March 2021
The COVID-19 disease has spread all over the world, representing an intriguing challenge for humanity as a whole. The efficient diagnosis of humans infected by COVID-19 still remains an increasing need worldwide. The chest X-ray imagery represents, among others, one attractive means to detect COVID-19 cases efficiently. Many studies have reported the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images. They conducted several comparisons among a subset of classifiers to identify the most accurate. In this paper, we investigate the potential of the combination of state-of-the-art classifiers in achieving the highest possible accuracy for the detection of COVID-19 from X-ray. For this purpose, we conducted a comprehensive comparison study among 16 state-of-the-art classifiers. To the best of our knowledge, this is the first study considering this number of classifiers. This paper’s innovation lies in the methodology that we followed to develop the inference system that allows us to detect COVID-19 with high accuracy. The methodology consists of three steps: (1) comprehensive comparative study between 16 state-of-the-art classifiers; (2) comparison between different ensemble classification techniques, including hard/soft majority, weighted voting, Support Vector Machine, and Random Forest; and (3) finding the combination of deep learning models and ensemble classification techniques that lead to the highest classification confidence on three classes. We found that using the Majority Voting approach is an adequate strategy to adopt in general cases for this task and may achieve an average accuracy up to 99.314%. View Full-Text
Keywords: COVID-19; X-ray; deep learning; classification; majority voting; Pneumonia; VGGNet; EfficientNet; ResNet; MobileNet; inception; densenet COVID-19; X-ray; deep learning; classification; majority voting; Pneumonia; VGGNet; EfficientNet; ResNet; MobileNet; inception; densenet
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MDPI and ACS Style

Ben Jabra, M.; Koubaa, A.; Benjdira, B.; Ammar, A.; Hamam, H. COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting. Appl. Sci. 2021, 11, 2884. https://doi.org/10.3390/app11062884

AMA Style

Ben Jabra M, Koubaa A, Benjdira B, Ammar A, Hamam H. COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting. Applied Sciences. 2021; 11(6):2884. https://doi.org/10.3390/app11062884

Chicago/Turabian Style

Ben Jabra, Marwa, Anis Koubaa, Bilel Benjdira, Adel Ammar, and Habib Hamam. 2021. "COVID-19 Diagnosis in Chest X-rays Using Deep Learning and Majority Voting" Applied Sciences 11, no. 6: 2884. https://doi.org/10.3390/app11062884

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