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Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging: a review

IAES International Journal of Artificial Intelligence · Vol. 13 No. 1 (2024) · pp. 648–657

DOI
10.11591/ijai.v13.i1.pp648-657
ISSN
2252-8938, 2089-4872
Published
2024-03-01
Publisher
Institute of Advanced Engineering and Science
Language
eng
License
creativecommons.org/licenses/by-sa/4.0

Abstract

Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.

Keywords

Collaboration network of the authors

30 authors · 95 co-authorship links · 18 across different organisations

Hover or tap a name to highlight its co-authors. Double-click to open the author's page.

  • Universiti Teknikal Malaysia Melaka
  • Universiti Putra Malaysia
  • Universitas Ahmad Dahlan
  • University of Osijek
  • Multimedia University
  • Affiliation unknown
  • Different organisations
  • Same organisation
  • Affiliation unknown
  • Thicker line = more joint articles · bigger circle = more articles
Show as table
Co-authorOrganisationJoint articles
Tole SutiknoUniversitas Ahmad Dahlan6
Mohd Hatta JopriUniversiti Teknikal Malaysia Melaka6
Mustafa ManapUniversiti Teknikal Malaysia Melaka6
Nursabillilah Mohd AliUniversiti Teknikal Malaysia Melaka6
M. Rahimi YusoffUniversiti Teknikal Malaysia Melaka5
N. S. M. NoorUniversiti Teknikal Malaysia Melaka4
Ezreen Farina ShairUniversiti Teknikal Malaysia Melaka4
Muhammad Izzat Ahmad SabriAffiliation unknown4
Nur Hasanah AliMultimedia University3
A.S. HussinUniversiti Teknikal Malaysia Melaka2
Mohd Ruddin Ab GhaniUniversiti Teknikal Malaysia Melaka2
Jingwei TooUniversiti Teknikal Malaysia Melaka2
Haslinda MusaUniversiti Teknikal Malaysia Melaka2
Nor Nabilah Syazana Abdul RahmanUniversiti Teknikal Malaysia Melaka2
Norihan Binti Abdul HamidUniversiti Teknikal Malaysia Melaka2
M. A. Muhammad SyahmiUniversiti Teknikal Malaysia Melaka2
M.F. HabbanUniversiti Teknikal Malaysia Melaka1
Muhamad Faizal YaakubUniversiti Teknikal Malaysia Melaka1
Weihown TeeAffiliation unknown1
Srete NikolovskiUniversity of Osijek1
Patriawan PutuAffiliation unknown1
Sitanggang FirmanAffiliation unknown1
Mohd Redzuan IsmailAffiliation unknown1
Nik Azuan Nik IsmailAffiliation unknown1
Nur Yazmin YaacobAffiliation unknown1

Affiliations come from OpenAlex; journal OAI-PMH metadata carries none. Authors without an OpenAlex ID are matched by name, which can merge or split people.

Metadata source: OAI-PMH (metadata jurnal) http://ijai.iaescore.com/index.php/IJAI/oai, first harvested 8 Nov 2026, last seen 8 Nov 2026.
Original metadata (oai_dc) 
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	<dc:title xml:lang="en-US">Segmentation and classification techniques used to detect early stroke diagnosis using brain magnetic resonance imaging:  a review</dc:title>
	<dc:creator>Kandaya, Shaarmila</dc:creator>
	<dc:creator>Abdullah, Abdul Rahim</dc:creator>
	<dc:creator>Saad, Norhashimah Mohd</dc:creator>
	<dc:creator>Muda, Ahmad Sobri</dc:creator>
	<dc:creator>Ahmad Sabri, Muhammad Izzat</dc:creator>
	<dc:subject xml:lang="en-US">Review; Segmentation:Classification</dc:subject>
	<dc:subject xml:lang="en-US">Cardiac pacemakers; Claustrophobia; Computed tomography scan; Disc disease; Magnetic resonance images; Qualitative losses;</dc:subject>
	<dc:description xml:lang="en-US">Stroke is a leading cause of disability and death worldwide. Early diagnosis and treatment are crucial in reducing the risk of stroke-related complications. Brain magnetic resonance imaging (MRI) is a common diagnostic tool used for stroke evaluation. However, manual interpretation of MRI images can be time-consuming and subjective. Machine learning (ML) algorithms have shown promise in automating and improving stroke diagnosis accuracy. This article focuses on classification and segmentation techniques used to detect early stroke diagnosis using brain magnetic imaging. The diagnosis, treatment, and prognosis of complications and patient outcomes in a number of neurological diseases are currently made possible by ML through pattern recognition algorithms. However, the use of MRI is limited because of MRI plays an important role in diagnosing lumbar disc disease. However, the use of MRI is limited due to its high cost and significant operational and processing time. More importantly, MRI is contraindicated in some patients who are claustrophobic or have pacemakers due to the potential for damage. Recent studies have shown that treatment within six hours of a stroke can save a patient's life. Unfortunately, Malaysia is facing a shortage of neuroradiologists, hampering efforts to treat its growing number of stroke patients.</dc:description>
	<dc:publisher xml:lang="en-US">Institute of Advanced Engineering and Science</dc:publisher>
	<dc:contributor xml:lang="en-US">elsevier</dc:contributor>
	<dc:date>2024-03-01</dc:date>
	<dc:type>info:eu-repo/semantics/article</dc:type>
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	<dc:format>application/pdf</dc:format>
	<dc:identifier>https://ijai.iaescore.com/index.php/IJAI/article/view/23548</dc:identifier>
	<dc:identifier>10.11591/ijai.v13.i1.pp648-657</dc:identifier>
	<dc:source xml:lang="en-US">IAES International Journal of Artificial Intelligence (IJ-AI); Vol 13, No 1: March 2024; 648-657</dc:source>
	<dc:source>2252-8938</dc:source>
	<dc:source>2089-4872</dc:source>
	<dc:source>10.11591/ijai.v13.i1</dc:source>
	<dc:language>eng</dc:language>
	<dc:relation>https://ijai.iaescore.com/index.php/IJAI/article/view/23548/13867</dc:relation>
	<dc:rights xml:lang="en-US">Copyright (c) 2023 Institute of Advanced Engineering and Science</dc:rights>
	<dc:rights xml:lang="en-US">http://creativecommons.org/licenses/by-sa/4.0</dc:rights>
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