Hybrid ensemble deep learning model for advancing ischemic brain stroke detection and classification in clinical application

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dc.authorscopusidRadwan Qasrawi / 57212263325
dc.authorwosidRadwan Qasrawi / AAA-6245-2019
dc.contributor.authorQasrawi, Radwan
dc.contributor.authorQdaih, Ibrahem
dc.contributor.authorDaraghmeh, Omar
dc.contributor.authorThwib, Suliman
dc.contributor.authorPolo, Stephanny Vicuna
dc.contributor.authorAtari, Siham
dc.contributor.authorAbu Al-Halawa, Diala
dc.date.accessioned2025-04-18T10:20:30Z
dc.date.available2025-04-18T10:20:30Z
dc.date.issued2024
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description.abstractIschemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model's performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings.
dc.identifier.citationQasrawi, R., Qdaih, I., Daraghmeh, O., Thwib, S., Vicuna Polo, S., Atari, S., & Abu Al-Halawa, D. (2024). Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application. Journal of Imaging, 10(7), 160.
dc.identifier.doi10.3390/jimaging10070160
dc.identifier.endpage15
dc.identifier.issn2313-433X
dc.identifier.issue7
dc.identifier.scopus2-s2.0-85199900101
dc.identifier.scopusqualityQ1
dc.identifier.startpage1
dc.identifier.urihttp://dx.doi.org/10.3390/jimaging10070160
dc.identifier.urihttps://hdl.handle.net/20.500.12713/7036
dc.identifier.volume10
dc.identifier.wosWOS:001277016800001
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorQasrawi, Radwan
dc.institutionauthoridRadwan Qasrawi / 0000-0001-8671-7026
dc.language.isoen
dc.publisherMDPI
dc.relation.ispartofJournal of imaging
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.subjectBrain Stroke
dc.subjectClinical Application
dc.subjectDeep Learning
dc.subjectHybrid Model
dc.subjectImage Enhancement
dc.subjectImages
dc.titleHybrid ensemble deep learning model for advancing ischemic brain stroke detection and classification in clinical application
dc.typeArticle

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