Automated cad system for early stroke diagnosis: Review

Stroke is an important health issue that affects millions of people globally each year. Early and precise stroke diagnosis is crucial for efficient treatment and better patient outcomes. Traditional stroke detection procedures, such as manual visual evaluation of clinical data, can be time-consu...

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Main Authors: Azman, Izzatul Husna, Mohd Saad, Norhashimah, Samsudin, Adam, Kandaya, Shaarmila, Hamzah, Rostam Affendi, Abdullah, Abdul Rahim
Format: Article
Language:English
Published: Science and Information Organization 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27114/2/0258720122023547.PDF
http://eprints.utem.edu.my/id/eprint/27114/
https://thesai.org/Downloads/Volume14No8/Paper_9-Automated_CAD_System_for_Early_Stroke_Diagnosis.pdf
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Institution: Universiti Teknikal Malaysia Melaka
Language: English
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spelling my.utem.eprints.271142024-06-19T15:21:41Z http://eprints.utem.edu.my/id/eprint/27114/ Automated cad system for early stroke diagnosis: Review Azman, Izzatul Husna Mohd Saad, Norhashimah Samsudin, Adam Kandaya, Shaarmila Hamzah, Rostam Affendi Abdullah, Abdul Rahim Stroke is an important health issue that affects millions of people globally each year. Early and precise stroke diagnosis is crucial for efficient treatment and better patient outcomes. Traditional stroke detection procedures, such as manual visual evaluation of clinical data, can be time-consuming and error-prone. Computer-aided diagnostic (CAD) technologieshave emerged as a viable option for early stroke diagnosis in recent years. These systems analyze medical pictures, such as magnetic resonance imaging (MRI), and identify indicators of stroke using modern algorithms and machine learning approaches. The goal of this review paper is to offer a thorough overview of the current state-of-the-art in CAD systems for early stroke detection. We give an examination of the merits and limits of this technology, as well as future research and development directions in this field. Finally, we contend that CAD systems represent a promising solution for improving the efficiency and accuracy of early stroke diagnosis, resulting in better patient outcomes and lower healthcare costs. Science and Information Organization 2023 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27114/2/0258720122023547.PDF Azman, Izzatul Husna and Mohd Saad, Norhashimah and Samsudin, Adam and Kandaya, Shaarmila and Hamzah, Rostam Affendi and Abdullah, Abdul Rahim (2023) Automated cad system for early stroke diagnosis: Review. International Journal of Advanced Computer Science and Applications, 14 (8). pp. 77-83. ISSN 2158-107X https://thesai.org/Downloads/Volume14No8/Paper_9-Automated_CAD_System_for_Early_Stroke_Diagnosis.pdf 10.14569/IJACSA.2023.0140809
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Stroke is an important health issue that affects millions of people globally each year. Early and precise stroke diagnosis is crucial for efficient treatment and better patient outcomes. Traditional stroke detection procedures, such as manual visual evaluation of clinical data, can be time-consuming and error-prone. Computer-aided diagnostic (CAD) technologieshave emerged as a viable option for early stroke diagnosis in recent years. These systems analyze medical pictures, such as magnetic resonance imaging (MRI), and identify indicators of stroke using modern algorithms and machine learning approaches. The goal of this review paper is to offer a thorough overview of the current state-of-the-art in CAD systems for early stroke detection. We give an examination of the merits and limits of this technology, as well as future research and development directions in this field. Finally, we contend that CAD systems represent a promising solution for improving the efficiency and accuracy of early stroke diagnosis, resulting in better patient outcomes and lower healthcare costs.
format Article
author Azman, Izzatul Husna
Mohd Saad, Norhashimah
Samsudin, Adam
Kandaya, Shaarmila
Hamzah, Rostam Affendi
Abdullah, Abdul Rahim
spellingShingle Azman, Izzatul Husna
Mohd Saad, Norhashimah
Samsudin, Adam
Kandaya, Shaarmila
Hamzah, Rostam Affendi
Abdullah, Abdul Rahim
Automated cad system for early stroke diagnosis: Review
author_facet Azman, Izzatul Husna
Mohd Saad, Norhashimah
Samsudin, Adam
Kandaya, Shaarmila
Hamzah, Rostam Affendi
Abdullah, Abdul Rahim
author_sort Azman, Izzatul Husna
title Automated cad system for early stroke diagnosis: Review
title_short Automated cad system for early stroke diagnosis: Review
title_full Automated cad system for early stroke diagnosis: Review
title_fullStr Automated cad system for early stroke diagnosis: Review
title_full_unstemmed Automated cad system for early stroke diagnosis: Review
title_sort automated cad system for early stroke diagnosis: review
publisher Science and Information Organization
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27114/2/0258720122023547.PDF
http://eprints.utem.edu.my/id/eprint/27114/
https://thesai.org/Downloads/Volume14No8/Paper_9-Automated_CAD_System_for_Early_Stroke_Diagnosis.pdf
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