An efficient CNN-BiLSTM model for multi-class intracranial hemorrhage classification

dc.contributor.advisorAkilan, Thangarajah
dc.contributor.authorGenereux, Kevin
dc.contributor.committeememberNaser, Hassan
dc.contributor.committeememberBajwa, Garima
dc.contributor.committeememberZhou, Yushi
dc.date.accessioned2023-09-21T15:16:47Z
dc.date.available2023-09-21T15:16:47Z
dc.date.created2023
dc.date.issued2023
dc.description.abstractIntracranial hemorrhage (ICH) refers to a type of bleeding that occurs within the skull. ICH may be caused by a wide range of pathology, including, trauma, hypertension, cerebral amyloid angiopa- thy, and cerebral aneurysms. Different subtypes of ICH exist based on their location in the brain, including epidural hemorrhage (EDH), subdural hemorrhage (SDH), subarachnoid hemorrhage (SAH), intraventricular hemorrhage (IVH), and intraparenchymal hemorrhage (IPH). Prompt de- tection and management of ICH are crucial as it is a life-threatening medical emergency with high morbidity and mortality rates. Despite accounting for only 10-15% of all strokes, ICH is respon- sible for over 50% of stroke-related deaths. Therefore, the presence, type, and location of an ICH must be immediately diagnosed so that the patients can receive medical intervention. However, accurately identifying ICH in CT slices can be challenging due to the brain’s complex anatomy and the variability in hemorrhage appearance. [...]en_US
dc.identifier.urihttps://knowledgecommons.lakeheadu.ca/handle/2453/5222
dc.language.isoen_USen_US
dc.subjectIntracranial hemorrhage (ICH)en_US
dc.subjectCT scansen_US
dc.subjectGraph Neural Networks (GNNs)en_US
dc.titleAn efficient CNN-BiLSTM model for multi-class intracranial hemorrhage classificationen_US
dc.typeThesisen_US
etd.degree.disciplineEngineering : Electrical & Computeren_US
etd.degree.grantorLakehead Universityen_US
etd.degree.levelMasteren_US
etd.degree.nameMaster of Scienceen_US

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