A Comparative Study on Subdural Brain Hemorrhage Segmentation

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dc.contributor.authorGençtürk, Tuğrul Hakan
dc.contributor.authorKaya, İsmail
dc.contributor.authorGülağız, Fidan Kaya
dc.date.accessioned2024-11-07T10:39:35Z
dc.date.available2024-11-07T10:39:35Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.descriptionInternational Conference on Computing, Intelligence and Data Analytics, ICCIDA 2022 -- 16 September 2022 through 17 September 2022 -- Kocaeli -- 291929
dc.description.abstractBrain hemorrhages are one of the most dangerous disease groups. If not detected early, it can lead to death or severe disability. The most common method used to detect bleeding is the evaluation of computed tomography (CT) images belonging to the bleeding area by specialist physicians. Considering the difficulty of access to neurosurgery specialists and the lack of expertise of other doctors in emergency intervention on the subject, there is a need for decision support mechanisms to assist physicians in the diagnosis and treatment process. Artificial intelligence-based systems to be used for this purpose can accelerate the diagnosis and treatment process while reducing the burden on physicians. In this study, the suitability of Mask Region-Based Convolutional Neural Network (Mask R-CNN), Cascade Region-Based Convolutional Neural Network (Cascade R-CNN), Mask Scoring Region-Based Convolutional Neural Network (MS R-CNN), Hybrid Task Cascade (HTC), You Only Look At Coefficients (YOLACT), Instances as Queries (QueryInst), and Sample Consistency Network (SCNet) methods, investigated for the problem of detection and segmentation of subdural brain hemorrhages. The performance of the methods was determined over the images in the CQ500 dataset. This is one of the few studies that perform segmentation of subdural cerebral hemorrhages using CT images from an open dataset. The results were evaluated according to Intersection Over Union (IoU) and Mean Average Precision (mAP) metrics. Experimental results showed that two methods could detect and segment subdural hemorrhages more accurately than the others. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.identifier.doi10.1007/978-3-031-27099-4_24
dc.identifier.endpage318
dc.identifier.isbn978-303127098-7
dc.identifier.issn2367-3370
dc.identifier.scopus2-s2.0-85151047742
dc.identifier.scopusqualityQ4
dc.identifier.startpage304
dc.identifier.urihttps://doi.org/10.1007/978-3-031-27099-4_24
dc.identifier.urihttps://hdl.handle.net/11480/11049
dc.identifier.volume643 LNNS
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.relation.ispartofLecture Notes in Networks and Systems
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241106
dc.subjectCNN
dc.subjectDeep learning
dc.subjectInstance segmentation
dc.subjectSubdural hemorrhage
dc.titleA Comparative Study on Subdural Brain Hemorrhage Segmentation
dc.typeConference Object

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