Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images
Stroke remains one of the leading causes of disability and mortality worldwide, necessitating timely and accurate diagnosis to improve treatment outcomes. This study presents a computer-aided diagnosis (CAD) system designed to detect and classify stroke lesions in magnetic resonance imaging (MRI),...
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| Language: | en |
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Accent Social and Welfare Society
2025
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| Online Access: | http://eprints.utem.edu.my/id/eprint/29556/2/00332220920251135222149.pdf http://eprints.utem.edu.my/id/eprint/29556/ https://accentsjournals.org/PaperDirectory/Journal/IJATEE/2025/8/5.pdf http://dx.doi.org/10.19101/IJATEE.2024.111102264 |
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| author | Mohd Saad, Norhashimah Azman, Izzatul Husna Abdullah, Abdul Rahim Hamzah, Rostam Affendi Muda, Ahmad Sobri Yamba, Farzanah Atikah |
| author_facet | Mohd Saad, Norhashimah Azman, Izzatul Husna Abdullah, Abdul Rahim Hamzah, Rostam Affendi Muda, Ahmad Sobri Yamba, Farzanah Atikah |
| author_sort | Mohd Saad, Norhashimah |
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| collection | Institutional Repository |
| content_provider | Universiti Teknikal Malaysia Melaka |
| content_source | UTEM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Stroke remains one of the leading causes of disability and mortality worldwide, necessitating timely and accurate
diagnosis to improve treatment outcomes. This study presents a computer-aided diagnosis (CAD) system designed to detect and classify stroke lesions in magnetic resonance imaging (MRI), specifically utilizing diffusion-weighted imaging (DWI) sequences. A hybrid segmentation technique, fuzzy c-means with active contour (FCMAC), is proposed to enhance lesion localization accuracy. For classification, the system evaluates traditional machine learning algorithms like support vector machine (SVM) and k-nearest neighbor (KNN), alongside deep learning models such as convolutional neural
network (CNN) and bilayered neural network (BNN). The entire diagnostic pipeline is integrated into a MATLAB-based
graphical user interface (GUI), facilitating real-time analysis and ease of use in clinical settings. Experimental results
show that the proposed FCMAC method achieves a dice coefficient (DC) of 0.654, outperforming conventional
segmentation techniques. Among the classifiers, KNN offered the best balance between prediction accuracy and
computational efficiency. The final system, termed SmartStroke-Pro, enables early detection and classification of stroke, providing a reliable and practical tool to assist healthcare professionals, particularly in resource-limited environments. This framework has the potential to reduce diagnostic delays and support improved clinical decision-making in acute stroke care. |
| format | Article |
| id | my.utem.eprints-29556 |
| institution | Universiti Teknikal Malaysia Melaka |
| language | en |
| publishDate | 2025 |
| publisher | Accent Social and Welfare Society |
| record_format | eprints |
| spelling | my.utem.eprints-295562026-02-23T04:44:52Z http://eprints.utem.edu.my/id/eprint/29556/ Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images Mohd Saad, Norhashimah Azman, Izzatul Husna Abdullah, Abdul Rahim Hamzah, Rostam Affendi Muda, Ahmad Sobri Yamba, Farzanah Atikah Stroke remains one of the leading causes of disability and mortality worldwide, necessitating timely and accurate diagnosis to improve treatment outcomes. This study presents a computer-aided diagnosis (CAD) system designed to detect and classify stroke lesions in magnetic resonance imaging (MRI), specifically utilizing diffusion-weighted imaging (DWI) sequences. A hybrid segmentation technique, fuzzy c-means with active contour (FCMAC), is proposed to enhance lesion localization accuracy. For classification, the system evaluates traditional machine learning algorithms like support vector machine (SVM) and k-nearest neighbor (KNN), alongside deep learning models such as convolutional neural network (CNN) and bilayered neural network (BNN). The entire diagnostic pipeline is integrated into a MATLAB-based graphical user interface (GUI), facilitating real-time analysis and ease of use in clinical settings. Experimental results show that the proposed FCMAC method achieves a dice coefficient (DC) of 0.654, outperforming conventional segmentation techniques. Among the classifiers, KNN offered the best balance between prediction accuracy and computational efficiency. The final system, termed SmartStroke-Pro, enables early detection and classification of stroke, providing a reliable and practical tool to assist healthcare professionals, particularly in resource-limited environments. This framework has the potential to reduce diagnostic delays and support improved clinical decision-making in acute stroke care. Accent Social and Welfare Society 2025 Article PeerReviewed text en cc_by_4 http://eprints.utem.edu.my/id/eprint/29556/2/00332220920251135222149.pdf Mohd Saad, Norhashimah and Azman, Izzatul Husna and Abdullah, Abdul Rahim and Hamzah, Rostam Affendi and Muda, Ahmad Sobri and Yamba, Farzanah Atikah (2025) Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images. International Journal of Advanced Technology and Engineering Exploration, 12 (129). pp. 1246-1263. ISSN 2394-7454 https://accentsjournals.org/PaperDirectory/Journal/IJATEE/2025/8/5.pdf http://dx.doi.org/10.19101/IJATEE.2024.111102264 |
| spellingShingle | Mohd Saad, Norhashimah Azman, Izzatul Husna Abdullah, Abdul Rahim Hamzah, Rostam Affendi Muda, Ahmad Sobri Yamba, Farzanah Atikah Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images |
| title | Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images |
| title_full | Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images |
| title_fullStr | Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images |
| title_full_unstemmed | Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images |
| title_short | Development of a CAD system for stroke diagnosis using machine learning on DWI-MRI images |
| title_sort | development of a cad system for stroke diagnosis using machine learning on dwi-mri images |
| url | http://eprints.utem.edu.my/id/eprint/29556/2/00332220920251135222149.pdf http://eprints.utem.edu.my/id/eprint/29556/ https://accentsjournals.org/PaperDirectory/Journal/IJATEE/2025/8/5.pdf http://dx.doi.org/10.19101/IJATEE.2024.111102264 |
| url_provider | http://eprints.utem.edu.my/ |