Deep Learning Models for Intracranial Hemorrhage Recognition: A comparative study

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dc.contributor.authorAmmar, Mohammed
dc.contributor.authorLamri, Mohamed Amine
dc.contributor.authorMahmoud, Saïd
dc.contributor.authorLaid, Amel
dc.date.accessioned2022-03-15T09:20:53Z
dc.date.available2022-03-15T09:20:53Z
dc.date.issued2022
dc.description.abstractEvery day, a large number of people with brain injury are received in the emergency rooms. Due to the large number of slices analyzed by the doctors for each patient and to accelerate the diagnosis, the development of a precise computer-aided diagnosis system becomes very recommended. The aim of our work is developing a tool to help radiologists in the detection of intracranial hemorrhage (ICH) and its five (05) subtypes in computed tomography (CT) images. Five deep learning models are tested: ResNet50, VGG16, Xception, InceptionV3 and InceptionResNetV2. Before training these models, preprocessing operations are performed like normalization and windowing. The experiments show that VGG-16 architecture provides the best performances. The model achieves an accuracy of 96%.en_US
dc.identifier.urihttps://doi.org/10.1016/j.procs.2021.12.031
dc.identifier.urihttps://dspace.univ-boumerdes.dz/handle/123456789/7709
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofseriesProcedia Computer Science/ Vol. 196 (2022);pp. 418–425
dc.subjectIntracranial Hemorrhageen_US
dc.subjectCTen_US
dc.subjectDetectionen_US
dc.subjectClassificationen_US
dc.subjectDeep Learningen_US
dc.subjectVGG-16en_US
dc.titleDeep Learning Models for Intracranial Hemorrhage Recognition: A comparative studyen_US
dc.typeArticleen_US

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