A survey of machine learning-based methods for COVID-19 medical image analysis

Sailunaz, Kashfia; Özyer, Tansel; Rokne, Jon; Alhajj, Reda

A survey of machine learning-based methods for COVID-19 medical image analysis

dc.authorid	0000-0001-6657-9738
dc.contributor.author	Sailunaz, Kashfia
dc.contributor.author	Özyer, Tansel
dc.contributor.author	Rokne, Jon
dc.contributor.author	Alhajj, Reda
dc.date.accessioned	2023-05-30T13:25:57Z
dc.date.available	2023-05-30T13:25:57Z
dc.date.issued	2023
dc.department	İstanbul Medipol Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstract	The ongoing COVID-19 pandemic caused by the SARS-CoV-2 virus has already resulted in 6.6 million deaths with more than 637 million people infected after only 30 months since the first occurrences of the disease in December 2019. Hence, rapid and accurate detection and diagnosis of the disease is the first priority all over the world. Researchers have been working on various methods for COVID-19 detection and as the disease infects lungs, lung image analysis has become a popular research area for detecting the presence of the disease. Medical images from chest X-rays (CXR), computed tomography (CT) images, and lung ultrasound images have been used by automated image analysis systems in artificial intelligence (AI)- and machine learning (ML)-based approaches. Various existing and novel ML, deep learning (DL), transfer learning (TL), and hybrid models have been applied for detecting and classifying COVID-19, segmentation of infected regions, assessing the severity, and tracking patient progress from medical images of COVID-19 patients. In this paper, a comprehensive review of some recent approaches on COVID-19-based image analyses is provided surveying the contributions of existing research efforts, the available image datasets, and the performance metrics used in recent works. The challenges and future research scopes to address the progress of the fight against COVID-19 from the AI perspective are also discussed. The main objective of this paper is therefore to provide a summary of the research works done in COVID detection and analysis from medical image datasets using ML, DL, and TL models by analyzing their novelty and efficiency while mentioning other COVID-19-based review/survey researches to deliver a brief overview on the maximum amount of information on COVID-19-based existing researches. [Figure not available: see fulltext.]
dc.identifier.citation	Sailunaz, K., Özyer, T., Rokne, J. ve Alhajj, R. (2023). A survey of machine learning-based methods for COVID-19 medical image analysis. Medical and Biological Engineering and Computing, 61(6), 1257-1297. https://doi.org/10.1007/s11517-022-02758-y
dc.identifier.doi	10.1007/s11517-022-02758-y
dc.identifier.endpage	1297
dc.identifier.issn	0140-0118
dc.identifier.issn	1741-0444
dc.identifier.issue	6
dc.identifier.pmid	36707488
dc.identifier.scopus	2-s2.0-85146953508
dc.identifier.scopusquality	Q2
dc.identifier.startpage	1257
dc.identifier.uri	https://doi.org/10.1007/s11517-022-02758-y
dc.identifier.uri	https://hdl.handle.net/20.500.12511/10996
dc.identifier.volume	61
dc.identifier.wos	000923099100001	en_US
dc.identifier.wosquality	Q2
dc.indekslendigikaynak	Web of Science
dc.indekslendigikaynak	Scopus
dc.indekslendigikaynak	PubMed
dc.institutionauthor	Alhajj, Reda
dc.language.iso	en
dc.publisher	Springer Science and Business Media Deutschland GmbH
dc.relation.ispartof	Medical and Biological Engineering and Computing	en_US
dc.relation.publicationcategory	Diğer
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	Computer Tomography
dc.subject	COVID-19
dc.subject	Deep Learning
dc.subject	Machine Learning
dc.subject	Medical Image Analysis
dc.subject	Transfer Learning
dc.title	A survey of machine learning-based methods for COVID-19 medical image analysis
dc.type	Review Article