CRANet: a comprehensive residual attention network for intracranial aneurysm image classification
Abstract Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneur...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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BMC
2022-08-01
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| Series: | BMC Bioinformatics |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12859-022-04872-y |
| _version_ | 1828444468371521536 |
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| author | Yawu Zhao Shudong Wang Yande Ren Yulin Zhang |
| author_facet | Yawu Zhao Shudong Wang Yande Ren Yulin Zhang |
| author_sort | Yawu Zhao |
| collection | DOAJ |
| description | Abstract Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms. |
| first_indexed | 2024-12-10T21:44:39Z |
| format | Article |
| id | doaj.art-aaa6e69ef8ad40e98a320ce91298e5c5 |
| institution | Directory Open Access Journal |
| issn | 1471-2105 |
| language | English |
| last_indexed | 2024-12-10T21:44:39Z |
| publishDate | 2022-08-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Bioinformatics |
| spelling | doaj.art-aaa6e69ef8ad40e98a320ce91298e5c52022-12-22T01:32:24ZengBMCBMC Bioinformatics1471-21052022-08-0123111510.1186/s12859-022-04872-yCRANet: a comprehensive residual attention network for intracranial aneurysm image classificationYawu Zhao0Shudong Wang1Yande Ren2Yulin Zhang3College of Computer Science and Technology, China University of PetroleumCollege of Computer Science and Technology, China University of PetroleumThe Department of Medical Imaging Center, The Affiliated Hospital of Qingdao UniversityCollege of Mathematics and System Science, Shandong University of Science and TechnologyAbstract Rupture of intracranial aneurysm is the first cause of subarachnoid hemorrhage, second only to cerebral thrombosis and hypertensive cerebral hemorrhage, and the mortality rate is very high. MRI technology plays an irreplaceable role in the early detection and diagnosis of intracranial aneurysms and supports evaluating the size and structure of aneurysms. The increase in many aneurysm images, may be a massive workload for the doctors, which is likely to produce a wrong diagnosis. Therefore, we proposed a simple and effective comprehensive residual attention network (CRANet) to improve the accuracy of aneurysm detection, using a residual network to extract the features of an aneurysm. Many experiments have shown that the proposed CRANet model could detect aneurysms effectively. In addition, on the test set, the accuracy and recall rates reached 97.81% and 94%, which significantly improved the detection rate of aneurysms.https://doi.org/10.1186/s12859-022-04872-yMedical image classificationDeep residual learningComprehensive attention mechanism |
| spellingShingle | Yawu Zhao Shudong Wang Yande Ren Yulin Zhang CRANet: a comprehensive residual attention network for intracranial aneurysm image classification BMC Bioinformatics Medical image classification Deep residual learning Comprehensive attention mechanism |
| title | CRANet: a comprehensive residual attention network for intracranial aneurysm image classification |
| title_full | CRANet: a comprehensive residual attention network for intracranial aneurysm image classification |
| title_fullStr | CRANet: a comprehensive residual attention network for intracranial aneurysm image classification |
| title_full_unstemmed | CRANet: a comprehensive residual attention network for intracranial aneurysm image classification |
| title_short | CRANet: a comprehensive residual attention network for intracranial aneurysm image classification |
| title_sort | cranet a comprehensive residual attention network for intracranial aneurysm image classification |
| topic | Medical image classification Deep residual learning Comprehensive attention mechanism |
| url | https://doi.org/10.1186/s12859-022-04872-y |
| work_keys_str_mv | AT yawuzhao cranetacomprehensiveresidualattentionnetworkforintracranialaneurysmimageclassification AT shudongwang cranetacomprehensiveresidualattentionnetworkforintracranialaneurysmimageclassification AT yanderen cranetacomprehensiveresidualattentionnetworkforintracranialaneurysmimageclassification AT yulinzhang cranetacomprehensiveresidualattentionnetworkforintracranialaneurysmimageclassification |