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Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning

Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing brain disorders, with stroke being a significant category among them. Recent studies emphasize the importance of swift treatment for stroke, known as "time is brain, " as early intervention within six hours of stroke onset...

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Main Authors: Kandaya, Shaarmila, Abdullah, Abdul Rahim, Mohd Saad, Norhashimah, Farina, Ezreen, Muda, Ahmad Sobri
格式: Article
语言:English
出版: Science and Information Organization 2024
在线阅读:http://psasir.upm.edu.my/id/eprint/113394/1/113394.pdf
http://psasir.upm.edu.my/id/eprint/113394/
https://thesai.org/Publications/ViewPaper?Volume=15&Issue=4&Code=IJACSA&SerialNo=47
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spelling my.upm.eprints.1133942024-11-22T06:20:30Z http://psasir.upm.edu.my/id/eprint/113394/ Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning Kandaya, Shaarmila Abdullah, Abdul Rahim Mohd Saad, Norhashimah Farina, Ezreen Muda, Ahmad Sobri Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing brain disorders, with stroke being a significant category among them. Recent studies emphasize the importance of swift treatment for stroke, known as "time is brain, " as early intervention within six hours of stroke onset can save lives and improve outcomes. However, the conventional manual diagnosis of brain stroke by neuroradiologists is subjective and time-consuming. To address this issue, this study presents an automatic technique for diagnosing and segmenting brain stroke from MRI images according to pre and post stroke patient. The technique utilizes machine learning methods, focusing on Susceptibility Weighted Imaging (SWI) sequences. The machine learning technique involves four stage, those are pre-processing, segmentation, feature extraction, and classification. In this paper, preprocessing and segmentation are proposed to identify the stroke region. The segmentation performance is assessed using Jaccard indices, Dice Coefficient, false positive, and false negative rates. The results show that adaptive threshold performs best for stroke lesion segmentation, with good improvement stroke patient that achieving the highest Dice coefficient of 0.96. In conclusion, this proposed stroke segmentation technique has promising potential for diagnosing early brain stroke, providing an efficient and automated approach to aid medical professionals in timely and accurate diagnoses. Science and Information Organization 2024 Article PeerReviewed text en http://psasir.upm.edu.my/id/eprint/113394/1/113394.pdf Kandaya, Shaarmila and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Farina, Ezreen and Muda, Ahmad Sobri (2024) Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning. International Journal of Advanced Computer Science and Applications, 15 (4). pp. 452-460. ISSN 2158-107X; eISSN: 2156-5570 https://thesai.org/Publications/ViewPaper?Volume=15&Issue=4&Code=IJACSA&SerialNo=47 10.14569/IJACSA.2024.0150447
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description Magnetic Resonance Imaging (MRI) plays a crucial role in diagnosing brain disorders, with stroke being a significant category among them. Recent studies emphasize the importance of swift treatment for stroke, known as "time is brain, " as early intervention within six hours of stroke onset can save lives and improve outcomes. However, the conventional manual diagnosis of brain stroke by neuroradiologists is subjective and time-consuming. To address this issue, this study presents an automatic technique for diagnosing and segmenting brain stroke from MRI images according to pre and post stroke patient. The technique utilizes machine learning methods, focusing on Susceptibility Weighted Imaging (SWI) sequences. The machine learning technique involves four stage, those are pre-processing, segmentation, feature extraction, and classification. In this paper, preprocessing and segmentation are proposed to identify the stroke region. The segmentation performance is assessed using Jaccard indices, Dice Coefficient, false positive, and false negative rates. The results show that adaptive threshold performs best for stroke lesion segmentation, with good improvement stroke patient that achieving the highest Dice coefficient of 0.96. In conclusion, this proposed stroke segmentation technique has promising potential for diagnosing early brain stroke, providing an efficient and automated approach to aid medical professionals in timely and accurate diagnoses.
format Article
author Kandaya, Shaarmila
Abdullah, Abdul Rahim
Mohd Saad, Norhashimah
Farina, Ezreen
Muda, Ahmad Sobri
spellingShingle Kandaya, Shaarmila
Abdullah, Abdul Rahim
Mohd Saad, Norhashimah
Farina, Ezreen
Muda, Ahmad Sobri
Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning
author_facet Kandaya, Shaarmila
Abdullah, Abdul Rahim
Mohd Saad, Norhashimah
Farina, Ezreen
Muda, Ahmad Sobri
author_sort Kandaya, Shaarmila
title Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning
title_short Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning
title_full Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning
title_fullStr Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning
title_full_unstemmed Segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (SWI) using machine learning
title_sort segmentation analysis for brain stroke diagnosis based on susceptibility-weighted imaging (swi) using machine learning
publisher Science and Information Organization
publishDate 2024
url http://psasir.upm.edu.my/id/eprint/113394/1/113394.pdf
http://psasir.upm.edu.my/id/eprint/113394/
https://thesai.org/Publications/ViewPaper?Volume=15&Issue=4&Code=IJACSA&SerialNo=47
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score 13.250345

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