Mitotic cell detection in histopathological images of neuroendocrine tumors using improved YOLOv5 by transformer mechanism

Basit Öğe Kaydı
dc.contributor.authorYucel, Zehra
dc.contributor.authorAkal, Fuat
dc.contributor.authorOltulu, Pembe
dc.date.accessioned2024-02-23T13:59:42Z
dc.date.available2024-02-23T13:59:42Z
dc.date.issued2023
dc.departmentNEÜen_US
dc.description.abstractAutomatic analysis of pathological images is important for the diagnosis and treatment of diseases. The use of computerized systems in this field is becoming increasingly common. Due to evolving technology and the speed of information needed, it is desirable for computers to be able to recognize objects like humans. Deep learning methods, which are a subfield of artificial intelligence, and image processing algorithms that recognize objects from images have been used in many fields in recent years, including healthcare. The aim of this study is to detect the mitoses in the histopathological images of neuroendocrine tumors using image processing methods based on deep learning. In our study, You Only Look Once-v5 (YOLOv5), the most widely used object recognition method, was used by combining the YOLOv5 transform module. YOLOv5 recognized mitotic cells with an accuracy of 0.80, a recall of 0.67, and an F1 score of 0.73, while the YOLOv5 transformer model recognized mitotic cells with an accuracy of 0.89, a recall of 0.68, and an F1 score of 0.77. The acceleration of the process and the objective evaluation will contribute significantly to an accurate and fast diagnosis. Another advantage is the time saved for pathologists, who can concentrate on important cases. In summary, automatic mitotic cell detection will facilitate tumor grade determination, treatment, and patient monitoring.en_US
dc.identifier.doi10.1007/s11760-023-02642-8
dc.identifier.endpage4114en_US
dc.identifier.issn1863-1703
dc.identifier.issn1863-1711
dc.identifier.issue8en_US
dc.identifier.scopus2-s2.0-85162041858en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage4107en_US
dc.identifier.urihttps://doi.org/10.1007/s11760-023-02642-8
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11282
dc.identifier.volume17en_US
dc.identifier.wosWOS:001012575100001en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringer London Ltden_US
dc.relation.ispartofSignal Image And Video Processingen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectBiomedical Image Processingen_US
dc.subjectHistopathological Imagesen_US
dc.subjectMitosis Detectionen_US
dc.subjectYolov5en_US
dc.subjectTransformeren_US
dc.titleMitotic cell detection in histopathological images of neuroendocrine tumors using improved YOLOv5 by transformer mechanismen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu