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Alzahrani A, Bhuiyan MAA, Akhter F. Detecting COVID-19 Pneumonia over Fuzzy Image Enhancement on Computed Tomography Images. COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE 2022; 2022:1043299. [PMID: 35087599 PMCID: PMC8789426 DOI: 10.1155/2022/1043299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/21/2021] [Revised: 10/30/2021] [Accepted: 12/01/2021] [Indexed: 11/30/2022]
Abstract
COVID-19 is the worst pandemic that has hit the globe in recent history, causing an increase in deaths. As a result of this pandemic, a number of research interests emerged in several fields such as medicine, health informatics, medical imaging, artificial intelligence and social sciences. Lung infection or pneumonia is the regular complication of COVID-19, and Reverse Transcription Polymerase Chain Reaction (RT-PCR) and computed tomography (CT) have played important roles to diagnose the disease. This research proposes an image enhancement method employing fuzzy expected value to improve the quality of the image for the detection of COVID-19 pneumonia. The principal objective of this research is to detect COVID-19 in patients using CT scan images collected from different sources, which include patients suffering from pneumonia and healthy people. The method is based on fuzzy histogram equalization and is organized with the improvement of the image contrast using fuzzy normalized histogram of the image. The effectiveness of the algorithm has been justified over several experiments on different features of CT images of lung for COVID-19 patients, like Ground-Glass Opacity (GGO), crazy paving, and consolidation. Experimental investigations indicate that among the 254 patients, 81.89% had features on both lungs; 9.5% on the left lung; and 10.24% on the right lung. The predominantly affected lobe was the right lower lobe (79.53%).
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Affiliation(s)
- Ali Alzahrani
- Department of Computer Engineering, King Faisal University, Hofuf 31982, Saudi Arabia
| | - Md. Al-Amin Bhuiyan
- Department of Computer Engineering, King Faisal University, Hofuf 31982, Saudi Arabia
| | - Fahima Akhter
- College of Applied Medical Sciences, King Faisal University, Hofuf 31982, Saudi Arabia
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Gudigar A, Raghavendra U, Nayak S, Ooi CP, Chan WY, Gangavarapu MR, Dharmik C, Samanth J, Kadri NA, Hasikin K, Barua PD, Chakraborty S, Ciaccio EJ, Acharya UR. Role of Artificial Intelligence in COVID-19 Detection. SENSORS (BASEL, SWITZERLAND) 2021; 21:8045. [PMID: 34884045 PMCID: PMC8659534 DOI: 10.3390/s21238045] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 11/26/2021] [Accepted: 11/26/2021] [Indexed: 12/15/2022]
Abstract
The global pandemic of coronavirus disease (COVID-19) has caused millions of deaths and affected the livelihood of many more people. Early and rapid detection of COVID-19 is a challenging task for the medical community, but it is also crucial in stopping the spread of the SARS-CoV-2 virus. Prior substantiation of artificial intelligence (AI) in various fields of science has encouraged researchers to further address this problem. Various medical imaging modalities including X-ray, computed tomography (CT) and ultrasound (US) using AI techniques have greatly helped to curb the COVID-19 outbreak by assisting with early diagnosis. We carried out a systematic review on state-of-the-art AI techniques applied with X-ray, CT, and US images to detect COVID-19. In this paper, we discuss approaches used by various authors and the significance of these research efforts, the potential challenges, and future trends related to the implementation of an AI system for disease detection during the COVID-19 pandemic.
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Affiliation(s)
- Anjan Gudigar
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - U Raghavendra
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Sneha Nayak
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chui Ping Ooi
- School of Science and Technology, Singapore University of Social Sciences, Singapore 599494, Singapore;
| | - Wai Yee Chan
- Department of Biomedical Imaging, Faculty of Medicine, University of Malaya, Kuala Lumpur 50603, Malaysia;
| | - Mokshagna Rohit Gangavarapu
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Chinmay Dharmik
- Department of Instrumentation and Control Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India; (A.G.); (S.N.); (M.R.G.); (C.D.)
| | - Jyothi Samanth
- Department of Cardiovascular Technology, Manipal College of Health Professions, Manipal Academy of Higher Education, Manipal 576104, India;
| | - Nahrizul Adib Kadri
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Khairunnisa Hasikin
- Department of Biomedical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur 50603, Malaysia; (N.A.K.); (K.H.)
| | - Prabal Datta Barua
- Cogninet Brain Team, Cogninet Australia, Sydney, NSW 2010, Australia;
- School of Business (Information Systems), Faculty of Business, Education, Law & Arts, University of Southern Queensland, Toowoomba, QLD 4350, Australia
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
| | - Subrata Chakraborty
- Faculty of Engineering and Information Technology, University of Technology Sydney, Sydney, NSW 2007, Australia;
- Faculty of Science, Agriculture, Business and Law, University of New England, Armidale, NSW 2351, Australia
| | - Edward J. Ciaccio
- Department of Medicine, Columbia University Medical Center, New York, NY 10032, USA;
| | - U. Rajendra Acharya
- School of Engineering, Ngee Ann Polytechnic, Singapore 599489, Singapore;
- Department of Biomedical Informatics and Medical Engineering, Asia University, Taichung 41354, Taiwan
- International Research Organization for Advanced Science and Technology (IROAST), Kumamoto University, Kumamoto 860-8555, Japan
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