research-article

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

Authors:
Roohallah Alizadehsani

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia

0000-0002-3069-7932
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,
Danial Sharifrazi

Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Sadra Hwy, Shiraz, Iran

Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Sadra Hwy, Shiraz, Iran
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,
Navid Hoseini Izadi

Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran

Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran
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,
Javad Hassannataj Joloudari

Department of Computer Engineering, Faculty of Engineering, University of Birjand, Daneshgah e Sanati Hwy Birjand, Iran

Department of Computer Engineering, Faculty of Engineering, University of Birjand, Daneshgah e Sanati Hwy Birjand, Iran
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,
Afshin Shoeibi

Computer Engineering Department, Ferdowsi University of Mashhad, Iran and Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran

Computer Engineering Department, Ferdowsi University of Mashhad, Iran and Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. N. Toosi University of Technology, Tehran, Iran
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,
Juan M. Gorriz

Department of Signal Theory, Networking and Communications, Universidad de Granada, Av. del Hospicio, Spain

Department of Signal Theory, Networking and Communications, Universidad de Granada, Av. del Hospicio, Spain
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,
Sadiq Hussain

System Administrator, Dibrugarh University, Assam, Dibrugarh, India

System Administrator, Dibrugarh University, Assam, Dibrugarh, India
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,
Juan E. Arco

Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Av. del Hospicio, Spain

Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Av. del Hospicio, Spain
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,
Zahra Alizadeh Sani

Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Iran and Omid Hospital, Iran University of Medical Sciences, Tehran, Iran

Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Iran and Omid Hospital, Iran University of Medical Sciences, Tehran, Iran
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,
Fahime Khozeimeh

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
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,
Abbas Khosravi

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
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,
Saeid Nahavandi

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia

Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia
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,
Sheikh Mohammed Shariful Islam

Institute for Physical Activity and Nutrition, Deakin University, Australia and Cardiovascular Division, The George Institute for Global Health, Australia and Sydney Medical School, University of Sydney, Sydney, Australia

Institute for Physical Activity and Nutrition, Deakin University, Australia and Cardiovascular Division, The George Institute for Global Health, Australia and Sydney Medical School, University of Sydney, Sydney, Australia
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,
U. Rajendra Acharya

Department of Electronics and Computer Engineering, Ngee AnnPolytechnic, Singapore and Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore and Department of Bioinformatics and Medical Engineering, Asia University, Taiwan

Department of Electronics and Computer Engineering, Ngee AnnPolytechnic, Singapore and Department of Biomedical Engineering, School of Science and Technology, Singapore University of Social Sciences, Singapore and Department of Bioinformatics and Medical Engineering, Asia University, Taiwan
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ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17 Issue 3sArticle No.: 104pp 1–24https://doi.org/10.1145/3462635

Published:15 November 2021Publication History

ACM Transactions on Multimedia Computing, Communications, and Applications

Abstract

The new coronavirus has caused more than one million deaths and continues to spread rapidly. This virus targets the lungs, causing respiratory distress which can be mild or severe. The X-ray or computed tomography (CT) images of lungs can reveal whether the patient is infected with COVID-19 or not. Many researchers are trying to improve COVID-19 detection using artificial intelligence. Our motivation is to develop an automatic method that can cope with scenarios in which preparing labeled data is time consuming or expensive. In this article, we propose a Semi-supervised Classification using Limited Labeled Data (SCLLD) relying on Sobel edge detection and Generative Adversarial Networks (GANs) to automate the COVID-19 diagnosis. The GAN discriminator output is a probabilistic value which is used for classification in this work. The proposed system is trained using 10,000 CT scans collected from Omid Hospital, whereas a public dataset is also used for validating our system. The proposed method is compared with other state-of-the-art supervised methods such as Gaussian processes. To the best of our knowledge, this is the first time a semi-supervised method for COVID-19 detection is presented. Our system is capable of learning from a mixture of limited labeled and unlabeled data where supervised learners fail due to a lack of sufficient amount of labeled data. Thus, our semi-supervised training method significantly outperforms the supervised training of Convolutional Neural Network (CNN) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 ± 0.20%, 99.88 ± 0.24%, and 99.40 ± 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 ± 4.11%, 91.2 ± 6.15%, and 46.40 ± 5.21%.

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Index Terms

Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data
1. Applied computing
  1. Life and medical sciences
    1. Health care information systems

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Published in
ACM Transactions on Multimedia Computing, Communications, and Applications Volume 17, Issue 3s
October 2021
324 pages
ISSN:1551-6857
EISSN:1551-6865
DOI:10.1145/3492435
Editor:
Alberto Del Bimbo
University of Firenze, Italy
Issue’s Table of Contents
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Publication History
- Published: 15 November 2021
- Accepted: 1 April 2021
- Revised: 1 March 2021
- Received: 1 December 2020
Published in tomm Volume 17, Issue 3s

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Author Tags
Semi-supervised learning
generative adversarial networks
COVID-19
supervised learning
deep learning
Qualifiers
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Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data

ACM Transactions on Multimedia Computing, Communications, and Applications

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