Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach
Background: Ischemic stroke poses significant challenges in diagnosis and treatment, necessitating efficient and accurate methods for assessing collateral circulation, a critical determinant of patient prognosis. Manual classification of collateral circulation in ischemic stroke using traditional im...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Multidisciplinary Digital Publishing Institute
2024
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| Online Access: | http://psasir.upm.edu.my/id/eprint/114567/ http://psasir.upm.edu.my/id/eprint/114567/1/114567.pdf |
| _version_ | 1848866532009967616 |
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| author | Ali, Nur Hasanah Abdullah, Abdul Rahim Mohd Saad, Norhashimah Muda, Ahmad Sobri Mhd Noor, Ervina Efzan |
| author_facet | Ali, Nur Hasanah Abdullah, Abdul Rahim Mohd Saad, Norhashimah Muda, Ahmad Sobri Mhd Noor, Ervina Efzan |
| author_sort | Ali, Nur Hasanah |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Background: Ischemic stroke poses significant challenges in diagnosis and treatment, necessitating efficient and accurate methods for assessing collateral circulation, a critical determinant of patient prognosis. Manual classification of collateral circulation in ischemic stroke using traditional imaging techniques is labor-intensive and prone to subjectivity. This study presented the automated classification of collateral circulation patterns in cone-beam CT (CBCT) images, utilizing the VGG11 architecture. Methods: The study utilized a dataset of CBCT images from ischemic stroke patients, accurately labeled with their respective collateral circulation status. To ensure uniformity and comparability, image normalization was executed during the preprocessing phase to standardize pixel values to a consistent scale or range. Then, the VGG11 model is trained using an augmented dataset and classifies collateral circulation patterns. Results: Performance evaluation of the proposed approach demonstrates promising results, with the model achieving an accuracy of 58.32%, a sensitivity of 75.50%, a specificity of 44.10%, a precision of 52.70%, and an F1 score of 62.10% in classifying collateral circulation patterns. Conclusions: This approach automates classification, potentially reducing diagnostic delays and improving patient outcomes. It also lays the groundwork for future research in using deep learning for better stroke diagnosis and management. This study is a significant advancement toward developing practical tools to assist doctors in making informed decisions for ischemic stroke patients. © 2024 by the authors. |
| first_indexed | 2025-11-15T14:22:05Z |
| format | Article |
| id | upm-114567 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T14:22:05Z |
| publishDate | 2024 |
| publisher | Multidisciplinary Digital Publishing Institute |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-1145672025-01-20T01:02:56Z http://psasir.upm.edu.my/id/eprint/114567/ Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach Ali, Nur Hasanah Abdullah, Abdul Rahim Mohd Saad, Norhashimah Muda, Ahmad Sobri Mhd Noor, Ervina Efzan Background: Ischemic stroke poses significant challenges in diagnosis and treatment, necessitating efficient and accurate methods for assessing collateral circulation, a critical determinant of patient prognosis. Manual classification of collateral circulation in ischemic stroke using traditional imaging techniques is labor-intensive and prone to subjectivity. This study presented the automated classification of collateral circulation patterns in cone-beam CT (CBCT) images, utilizing the VGG11 architecture. Methods: The study utilized a dataset of CBCT images from ischemic stroke patients, accurately labeled with their respective collateral circulation status. To ensure uniformity and comparability, image normalization was executed during the preprocessing phase to standardize pixel values to a consistent scale or range. Then, the VGG11 model is trained using an augmented dataset and classifies collateral circulation patterns. Results: Performance evaluation of the proposed approach demonstrates promising results, with the model achieving an accuracy of 58.32%, a sensitivity of 75.50%, a specificity of 44.10%, a precision of 52.70%, and an F1 score of 62.10% in classifying collateral circulation patterns. Conclusions: This approach automates classification, potentially reducing diagnostic delays and improving patient outcomes. It also lays the groundwork for future research in using deep learning for better stroke diagnosis and management. This study is a significant advancement toward developing practical tools to assist doctors in making informed decisions for ischemic stroke patients. © 2024 by the authors. Multidisciplinary Digital Publishing Institute 2024-07-08 Article PeerReviewed text en cc_by_4 http://psasir.upm.edu.my/id/eprint/114567/1/114567.pdf Ali, Nur Hasanah and Abdullah, Abdul Rahim and Mohd Saad, Norhashimah and Muda, Ahmad Sobri and Mhd Noor, Ervina Efzan (2024) Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach. BioMedInformatics, 4 (3). pp. 1692-1702. ISSN 2673-7426; eISSN: 2673-7426 https://www.mdpi.com/2673-7426/4/3/91 10.3390/biomedinformatics4030091 |
| spellingShingle | Ali, Nur Hasanah Abdullah, Abdul Rahim Mohd Saad, Norhashimah Muda, Ahmad Sobri Mhd Noor, Ervina Efzan Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach |
| title | Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach |
| title_full | Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach |
| title_fullStr | Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach |
| title_full_unstemmed | Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach |
| title_short | Automated classification of collateral circulation for ischemic stroke in cone-beam CT images using VGG11: a deep learning approach |
| title_sort | automated classification of collateral circulation for ischemic stroke in cone-beam ct images using vgg11: a deep learning approach |
| url | http://psasir.upm.edu.my/id/eprint/114567/ http://psasir.upm.edu.my/id/eprint/114567/ http://psasir.upm.edu.my/id/eprint/114567/ http://psasir.upm.edu.my/id/eprint/114567/1/114567.pdf |