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Detection of Cerebral Ischaemia using Transfer Learning Techniques

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dc.contributor.authorAntón Munárriz, Cristina
dc.contributor.authorHaut, Juan M.
dc.contributor.authorPaoletti, Mercedes E.
dc.contributor.authorBenítez Andrades, José Alberto
dc.contributor.authorPastor Vargas, Rafael
dc.contributor.authorRobles Gómez, Antonio
dc.date.accessioned2024-05-20T12:03:15Z
dc.date.available2024-05-20T12:03:15Z
dc.description.abstractCerebrovascular accident (CVA) or stroke is one of the main causes of mortality and morbidity today, causing permanent disabilities. Its early detection helps reduce its effects and its mortality: time is brain. Currently, non-contrast computed tomography (NCCT) continues to be the first-line diagnostic method in stroke emergencies because it is a fast, available, and cost-effective technique that makes it possible to rule out haemorrhage and focus attention on the ischemic origin, that is, due to obstruction to arterial flow. NCCT are quantified using a scoring system called ASPECTS (Alberta Stroke Program Early Computed Tomography Score) according to the affected brain structures. This paper aims to detect in an initial phase those CTs of patients with stroke symptoms that present early alterations in CT density using a binary classifier of CTs without and with stroke, to alert the doctor of their existence. For this, several well-known neural network architectures are implemented in the ImageNet challenges (VGG, NasNet, ResNet and DenseNet), with 3D images, covering the entire brain volume. The training results of these networks are exposed, in which different parameters are tested to obtain maximum performance, which is achieved with a DenseNet3D network that achieves an accuracy of 98% in the training set and 95% in the test seten
dc.description.versionversión final
dc.identifier.urihttps://hdl.handle.net/20.500.14468/12978
dc.language.isoen
dc.publisherIEEE
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.congress36th International Symposium on Computer-Based Medical Systems (CBMS)
dc.relation.departmentSistemas de Comunicación y Control
dc.rightsinfo:eu-repo/semantics/embargoedAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0
dc.subject.keywordsCerebral Ischaemia
dc.subject.keywordsComputed tomography
dc.subject.keywordsDeep Learning
dc.subject.keywordsTransfer Learning
dc.subject.keywordsIctus Dataset
dc.titleDetection of Cerebral Ischaemia using Transfer Learning Techniqueses
dc.typeconference proceedingsen
dc.typeactas de congresoes
dspace.entity.typePublication
relation.isAuthorOfPublicationf93103de-336d-47ac-886b-e2cbd425ed87
relation.isAuthorOfPublication17556659-f434-4220-841d-aac35f492e62
relation.isAuthorOfPublication.latestForDiscoveryf93103de-336d-47ac-886b-e2cbd425ed87
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