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Appl."],"published-print":{"date-parts":[[2021,10,31]]},"abstract":"<jats:p>\n            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\n            <jats:bold>computed tomography<\/jats:bold>\n            (\n            <jats:bold>CT<\/jats:bold>\n            ) 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\n            <jats:bold>Semi-supervised Classification using Limited Labeled Data<\/jats:bold>\n            (\n            <jats:bold>SCLLD<\/jats:bold>\n            ) relying on Sobel edge detection and\n            <jats:bold>Generative Adversarial Networks<\/jats:bold>\n            (\n            <jats:bold>GANs<\/jats:bold>\n            ) 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\n            <jats:bold>Convolutional Neural Network<\/jats:bold>\n            (\n            <jats:bold>CNN<\/jats:bold>\n            ) when labeled training data is scarce. The 95% confidence intervals for our method in terms of accuracy, sensitivity, and specificity are 99.56 \u00b1 0.20%, 99.88 \u00b1 0.24%, and 99.40 \u00b1 0.18%, respectively, whereas intervals for the CNN (trained supervised) are 68.34 \u00b1 4.11%, 91.2 \u00b1 6.15%, and 46.40 \u00b1 5.21%.\n          <\/jats:p>","DOI":"10.1145\/3462635","type":"journal-article","created":{"date-parts":[[2021,11,15]],"date-time":"2021-11-15T17:56:12Z","timestamp":1636998972000},"page":"1-24","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":45,"title":["Uncertainty-Aware Semi-Supervised Method Using Large Unlabeled and Limited Labeled COVID-19 Data"],"prefix":"10.1145","volume":"17","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3069-7932","authenticated-orcid":false,"given":"Roohallah","family":"Alizadehsani","sequence":"first","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia"}]},{"given":"Danial","family":"Sharifrazi","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, School of Technical and Engineering, Shiraz Branch, Islamic Azad University, Sadra Hwy, Shiraz, Iran"}]},{"given":"Navid Hoseini","family":"Izadi","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, Isfahan University of Technology, Isfahan, Iran"}]},{"given":"Javad Hassannataj","family":"Joloudari","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Faculty of Engineering, University of Birjand, Daneshgah e Sanati Hwy Birjand, Iran"}]},{"given":"Afshin","family":"Shoeibi","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Ferdowsi University of Mashhad, Iran and Faculty of Electrical and Computer Engineering, Biomedical Data Acquisition Lab, K. 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Toosi University of Technology, Tehran, Iran"}]},{"given":"Juan M.","family":"Gorriz","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Networking and Communications, Universidad de Granada, Av. del Hospicio, Spain"}]},{"given":"Sadiq","family":"Hussain","sequence":"additional","affiliation":[{"name":"System Administrator, Dibrugarh University, Assam, Dibrugarh, India"}]},{"given":"Juan E.","family":"Arco","sequence":"additional","affiliation":[{"name":"Department of Signal Theory, Networking and Communications, Universidad de Granada, Granada, Av. del Hospicio, Spain"}]},{"given":"Zahra Alizadeh","family":"Sani","sequence":"additional","affiliation":[{"name":"Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Iran and Omid Hospital, Iran University of Medical Sciences, Tehran, Iran"}]},{"given":"Fahime","family":"Khozeimeh","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia"}]},{"given":"Abbas","family":"Khosravi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia"}]},{"given":"Saeid","family":"Nahavandi","sequence":"additional","affiliation":[{"name":"Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Victoria, Australia"}]},{"given":"Sheikh Mohammed Shariful","family":"Islam","sequence":"additional","affiliation":[{"name":"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"}]},{"given":"U. Rajendra","family":"Acharya","sequence":"additional","affiliation":[{"name":"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"}]}],"member":"320","published-online":{"date-parts":[[2021,11,15]]},"reference":[{"key":"e_1_3_1_2_2","unstructured":"2021. Retrieved May 1 2021 from https:\/\/www.who.int\/docs\/default-source\/coronaviruse\/situation-reports\/20200513-covid-19-sitrep-114.pdf?sfvrsn=17ebbbe_4."},{"key":"e_1_3_1_3_2","unstructured":"2021. Retrieved May 1 2021 from https:\/\/www.kaggle.com\/bayazjafarli\/covid19-covidpneumanianormal-cases."},{"key":"e_1_3_1_4_2","unstructured":"2021. Retrieved May 1 2021 from https:\/\/coronavirus.jhu.edu\/map.html."},{"key":"e_1_3_1_5_2","unstructured":"2021. 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Journal of Medical Virology 93 (2020), 2307\u20132320.","journal-title":"Journal of Medical Virology"},{"key":"e_1_3_1_8_2","first-page":"104095","article-title":"Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991\u20132020","author":"Alizadehsani Roohallah","year":"2020","unstructured":"Roohallah Alizadehsani, Abbas Khosravi, Mohamad Roshanzamir, Moloud Abdar, Nizal Sarrafzadegan, Davood Shafie, Fahime Khozeimeh, Afshin Shoeibi, Saeid Nahavandi, Maryam Panahiazar, et\u00a0al. 2020. Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991\u20132020. 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