A review on computer aided diagnosis of acute brain stroke
Article
Inamdar, Mahesh Anil, Raghavendra, Udupi, Gudigar, Anjan, Chakole, Yashas, Hegde, Ajay, Menon, Girish R., Barua, Prabal, Palmer, Elizabeth Emma, Cheong, Kang Hao, Chan, Wai Yee, Ciaccio, Edward J. and Acharya, U. Rajendra. 2021. "A review on computer aided diagnosis of acute brain stroke." Sensors. 21 (24). https://doi.org/10.3390/s21248507
Article Title | A review on computer aided diagnosis of acute brain stroke |
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ERA Journal ID | 34304 |
Article Category | Article |
Authors | Inamdar, Mahesh Anil, Raghavendra, Udupi, Gudigar, Anjan, Chakole, Yashas, Hegde, Ajay, Menon, Girish R., Barua, Prabal, Palmer, Elizabeth Emma, Cheong, Kang Hao, Chan, Wai Yee, Ciaccio, Edward J. and Acharya, U. Rajendra |
Journal Title | Sensors |
Journal Citation | 21 (24) |
Article Number | 8507 |
Number of Pages | 35 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 1424-8220 |
1424-8239 | |
Digital Object Identifier (DOI) | https://doi.org/10.3390/s21248507 |
Web Address (URL) | https://www.mdpi.com/1424-8220/21/24/8507 |
Abstract | Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta–Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas. |
Keywords | CAD; Ischemic brain stroke; machine learning; deep learning |
ANZSRC Field of Research 2020 | 400306. Computational physiology |
Byline Affiliations | Manipal Academy of Higher Education, India |
School of Business | |
University of Technology Sydney | |
Cogninet Australia, Australia | |
University of New South Wales | |
Singapore University of Technology and Design | |
University of Malaya, Malaysia | |
Columbia University, United States | |
Ngee Ann Polytechnic, Singapore | |
Singapore University of Social Sciences (SUSS), Singapore | |
Asia University, Taiwan |
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https://research.usq.edu.au/item/z1v9y/a-review-on-computer-aided-diagnosis-of-acute-brain-stroke
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