A revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model

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dc.authoridKAYA, Ismail/0000-0002-4128-5845
dc.contributor.authorKaya, Ismail
dc.contributor.authorGencturk, Tugrul Hakan
dc.contributor.authorGulagiz, Fidan Kaya
dc.date.accessioned2024-11-07T13:32:16Z
dc.date.available2024-11-07T13:32:16Z
dc.date.issued2023
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstractBACKGROUND: The article was planned to make the first evaluation in terms of acute subdural hemorrhages, thinking that it can help in appropriate pathologies by tomography interpretation with the artificial intelligence (AI) method, at least in a way to quickly warn the responsible doctor.METHODS: A two-level AI-based hybrid method was developed. The proposed model uses the mask-region convolutional neural network (Mask R-CNN) technique, which is a deep learning model, in the hemorrhagic region's mask generation stage, and a problem -specific, optimized support vector machines (SVM) technique which is a machine learning model in the binary classification stage. Furthermore, the bee colony algorithm was used for the optimization of SVM algorithms' parameters.RESULTS: In the first stage, the mean average precision (mAP) value was obtained as 0.754 when the intercept over union (IOU) value was taken as 0.5 with the Mask R-CNN architecture used. At the same time, when a 5-fold cross-validation was applied, the mAP value was obtained 0.736. With the hyperparameter optimization for both Mask R-CNN and the SVM algorithm, the accuracy of the two-level classification process was obtained as 96.36%. Furthermore, final false-negative rate and false-positive rate values were obtained as 6.20%, and 2.57%, respectively.CONCLUSION: With the proposed model, both the detection of hemorrhage and the presentation of the suspicious area to the physician were performed more successfully on two dimensional (2D) images with low cost and high accuracy compared to similar studies and today's interpretations with telemedicine techniques.
dc.identifier.doi10.14744/tjtes.2023.76756
dc.identifier.endpage871
dc.identifier.issn1306-696X
dc.identifier.issn1307-7945
dc.identifier.issue8
dc.identifier.pmid37563894
dc.identifier.scopus2-s2.0-85167675513
dc.identifier.scopusqualityQ2
dc.identifier.startpage858
dc.identifier.trdizinid1263613
dc.identifier.urihttps://doi.org/10.14744/tjtes.2023.76756
dc.identifier.urihttps://search.trdizin.gov.tr/tr/yayin/detay/1263613
dc.identifier.urihttps://hdl.handle.net/11480/15324
dc.identifier.volume29
dc.identifier.wosWOS:001124235500008
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakTR-Dizin
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherTurkish Assoc Trauma Emergency Surgery
dc.relation.ispartofUlusal Travma Ve Acil Cerrahi Dergisi-Turkish Journal of Trauma & Emergency Surgery
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectAcute subdural hematoma
dc.subjectartificial intelligence
dc.subjectearly diagnosis
dc.titleA revolutionary acute subdural hematoma detection based on two-tiered artificial intelligence model
dc.title.alternativeİki katmanlı yapay zeka modeline dayalı devrimsel akut subdural hematom tespiti
dc.typeArticle

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