A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach
The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020. It has been spreading ever since, and the peak specific to every country h...
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| Format: | Article |
| Language: | English |
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INTI International University
2021
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| Online Access: | http://eprints.intimal.edu.my/1527/ http://eprints.intimal.edu.my/1527/1/vol.2021_004.pdf |
| _version_ | 1848766762341892096 |
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| author | Noor, Al-dieef Shabana, Habib |
| author_facet | Noor, Al-dieef Shabana, Habib |
| author_sort | Noor, Al-dieef |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020. It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers’ focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic. |
| first_indexed | 2025-11-14T11:56:18Z |
| format | Article |
| id | intimal-1527 |
| institution | INTI International University |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-14T11:56:18Z |
| publishDate | 2021 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-15272021-08-26T10:52:44Z http://eprints.intimal.edu.my/1527/ A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach Noor, Al-dieef Shabana, Habib QA75 Electronic computers. Computer science QA76 Computer software The COVID-19 pandemic (the form of coronaviruses) developed at the end of 2019 and spread rapidly to almost every corner of the world. It has infected around 25,334,339 of the world population by the end of September 1, 2020. It has been spreading ever since, and the peak specific to every country has been rising and falling and does not seem to be over yet. Currently, the conventional RT-PCR testing is required to detect COVID-19, but the alternative method for data archiving purposes is certainly another choice for public departments to make. Researchers are trying to use medical images such as X-ray and Computed Tomography (CT) to easily diagnose the virus with the aid of Artificial Intelligence (AI)-based software. This review paper provides an investigation of a newly emerging machine-learning method used to detect COVID-19 from X-ray images instead of using other methods of tests performed by medical experts. The facilities of computer vision enable us to develop an automated model that has clinical abilities of early detection of the disease. We have explored the researchers’ focus on the modalities, images of datasets for use by the machine learning methods, and output metrics used to test the research in this field. Finally, the paper concludes by referring to the key problems posed by identifying COVID-19 using machine learning and future work studies. This review's findings can be useful for public and private sectors to utilize the X-ray images and deployment of resources before the pandemic can reach its peaks, enabling the healthcare system with cushion time to bear the impact of the unfavorable circumstances of the pandemic. INTI International University 2021-09 Article PeerReviewed text en http://eprints.intimal.edu.my/1527/1/vol.2021_004.pdf Noor, Al-dieef and Shabana, Habib (2021) A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach. Journal of Data Science, 2021 (04). ISSN 2805-5160 https://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software Noor, Al-dieef Shabana, Habib A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach |
| title | A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach |
| title_full | A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach |
| title_fullStr | A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach |
| title_full_unstemmed | A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach |
| title_short | A Review on Detection of COVID-19 Cases from Medical Images Using Machine Learning-Based Approach |
| title_sort | review on detection of covid-19 cases from medical images using machine learning-based approach |
| topic | QA75 Electronic computers. Computer science QA76 Computer software |
| url | http://eprints.intimal.edu.my/1527/ http://eprints.intimal.edu.my/1527/ http://eprints.intimal.edu.my/1527/1/vol.2021_004.pdf |