FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3163971970> ?p ?o ?g. }

• W3163971970 endingPage "522" @default.
• W3163971970 startingPage "512" @default.
• W3163971970 abstract "Background Moyamoya disease (MMD) is a serious intracranial cerebrovascular disease. Cerebral hemorrhage caused by MMD will bring life risk to patients. Therefore, MMD detection is of great significance in the prevention of cerebral hemorrhage. In order to improve the accuracy of digital subtraction angiography (DSA) in the diagnosis of ischemic MMD, in this paper, a deep network architecture combined with 3D convolutional neural network (3D CNN) and bidirectional convolutional gated recurrent unit (BiConvGRU) is proposed to learn the spatiotemporal features for ischemic MMD detection.Methods Firstly, 2D convolutional neural network (2D CNN) is utilized to extract spatial features for each frame of DSA. Secondly, the long-term spatiotemporal features of DSA sequence are extracted by BiConvGRU. Thirdly, the short-term spatiotemporal features of DSA are further extracted by 3D convolutional neural network (3D CNN). In addition, different features are extracted when gray images and optical flow images pass through the network, and multiple features are extracted by features fusion. Finally, the fused features are utilized to classify.Results The proposed method was quantitatively evaluated on a data sets of 630 cases. The experimental results showed a detection accuracy of 0.9788, sensitivity and specificity were 0.9780 and 0.9796, respectively, and area under curve (AUC) was 0.9856. Compared with other methods, we can get the highest accuracy and AUC.Conclusions The experimental results show that the proposed method is stable and reliable for ischemic MMD detection, which provides an option for doctors to accurately diagnose ischemic MMD." @default.
• W3163971970 created "2021-06-07" @default.
• W3163971970 creator A5004081593 @default.
• W3163971970 creator A5026449840 @default.
• W3163971970 creator A5036412388 @default.
• W3163971970 creator A5056815920 @default.
• W3163971970 creator A5063717271 @default.
• W3163971970 creator A5065418938 @default.
• W3163971970 creator A5066091502 @default.
• W3163971970 creator A5080019802 @default.
• W3163971970 creator A5080636854 @default.
• W3163971970 date "2021-11-23" @default.
• W3163971970 modified "2023-10-16" @default.
• W3163971970 title "Learning spatiotemporal features of DSA using 3D CNN and BiConvGRU for ischemic moyamoya disease detection" @default.
• W3163971970 cites W1522734439 @default.
• W3163971970 cites W1899329716 @default.
• W3163971970 cites W2035121886 @default.
• W3163971970 cites W2041625018 @default.
• W3163971970 cites W2044783238 @default.
• W3163971970 cites W2047026967 @default.
• W3163971970 cites W2060894785 @default.
• W3163971970 cites W2064675550 @default.
• W3163971970 cites W2112796928 @default.
• W3163971970 cites W2117728036 @default.
• W3163971970 cites W2283270737 @default.
• W3163971970 cites W2405546895 @default.
• W3163971970 cites W2508429489 @default.
• W3163971970 cites W2595328592 @default.
• W3163971970 cites W2769039400 @default.
• W3163971970 cites W2884481078 @default.
• W3163971970 cites W2890144450 @default.
• W3163971970 cites W2904136839 @default.
• W3163971970 cites W2908227877 @default.
• W3163971970 cites W2920052808 @default.
• W3163971970 cites W2944120591 @default.
• W3163971970 cites W2963073938 @default.
• W3163971970 cites W2963471864 @default.
• W3163971970 cites W2964101993 @default.
• W3163971970 cites W2965655638 @default.
• W3163971970 cites W3013533105 @default.
• W3163971970 cites W3021357433 @default.
• W3163971970 cites W3022595769 @default.
• W3163971970 cites W3028198339 @default.
• W3163971970 doi "https://doi.org/10.1080/00207454.2021.1929214" @default.
• W3163971970 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/34042552" @default.
• W3163971970 hasPublicationYear "2021" @default.
• W3163971970 type Work @default.
• W3163971970 sameAs 3163971970 @default.
• W3163971970 citedByCount "6" @default.
• W3163971970 countsByYear W31639719702022 @default.
• W3163971970 countsByYear W31639719702023 @default.
• W3163971970 crossrefType "journal-article" @default.
• W3163971970 hasAuthorship W3163971970A5004081593 @default.
• W3163971970 hasAuthorship W3163971970A5026449840 @default.
• W3163971970 hasAuthorship W3163971970A5036412388 @default.
• W3163971970 hasAuthorship W3163971970A5056815920 @default.
• W3163971970 hasAuthorship W3163971970A5063717271 @default.
• W3163971970 hasAuthorship W3163971970A5065418938 @default.
• W3163971970 hasAuthorship W3163971970A5066091502 @default.
• W3163971970 hasAuthorship W3163971970A5080019802 @default.
• W3163971970 hasAuthorship W3163971970A5080636854 @default.
• W3163971970 hasConcept C108583219 @default.
• W3163971970 hasConcept C126838900 @default.
• W3163971970 hasConcept C153180895 @default.
• W3163971970 hasConcept C154945302 @default.
• W3163971970 hasConcept C2776548475 @default.
• W3163971970 hasConcept C2778286760 @default.
• W3163971970 hasConcept C2780643987 @default.
• W3163971970 hasConcept C41008148 @default.
• W3163971970 hasConcept C71924100 @default.
• W3163971970 hasConcept C81363708 @default.
• W3163971970 hasConceptScore W3163971970C108583219 @default.
• W3163971970 hasConceptScore W3163971970C126838900 @default.
• W3163971970 hasConceptScore W3163971970C153180895 @default.
• W3163971970 hasConceptScore W3163971970C154945302 @default.
• W3163971970 hasConceptScore W3163971970C2776548475 @default.
• W3163971970 hasConceptScore W3163971970C2778286760 @default.
• W3163971970 hasConceptScore W3163971970C2780643987 @default.
• W3163971970 hasConceptScore W3163971970C41008148 @default.
• W3163971970 hasConceptScore W3163971970C71924100 @default.
• W3163971970 hasConceptScore W3163971970C81363708 @default.
• W3163971970 hasFunder F4320321001 @default.
• W3163971970 hasIssue "5" @default.
• W3163971970 hasLocation W31639719701 @default.
• W3163971970 hasLocation W31639719702 @default.
• W3163971970 hasOpenAccess W3163971970 @default.
• W3163971970 hasPrimaryLocation W31639719701 @default.
• W3163971970 hasRelatedWork W2731899572 @default.
• W3163971970 hasRelatedWork W2999805992 @default.
• W3163971970 hasRelatedWork W3011074480 @default.
• W3163971970 hasRelatedWork W3116150086 @default.
• W3163971970 hasRelatedWork W3133861977 @default.
• W3163971970 hasRelatedWork W3192840557 @default.
• W3163971970 hasRelatedWork W4200173597 @default.
• W3163971970 hasRelatedWork W4291897433 @default.
• W3163971970 hasRelatedWork W4312417841 @default.
• W3163971970 hasRelatedWork W4321369474 @default.
• W3163971970 hasVolume "133" @default.

• next