Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors

Basit Öğe Kaydı
dc.authorid0000-0002-2917-3736en_US
dc.contributor.authorGündüz, Emrah
dc.contributor.authorAlçin, Ömer Faruk
dc.contributor.authorKızılay, Ahmeten_US
dc.contributor.authorYıldırım, İsmail Okanen_US
dc.date.accessioned2022-07-25T11:56:23Z
dc.date.available2022-07-25T11:56:23Z
dc.date.issued2022en_US
dc.departmentMTÖ Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümüen_US
dc.descriptionReceived: 29 April 2022 / Accepted: 16 May 2022en_US
dc.descriptionEmrah Gunduz, Department of Otorhinolaryngology Head and Neck Surgery, Malatya Training and Research Hospital, Malatya, Turkey. Omer Faruk Alçin, Department of Electric and Electronics Engineering Faculty of Engineering and Natural Sciences Malatya, Turgut Ozal University Malatya, Malatya, Turkey. Ahmet Kizilay, Department of Otorhinolaryngology Head and Neck Surgery, Inonu University Faculty of Medicine, Malatya, 44000, Turkey. Ismail Okan Yildirim, Department of Radiology, Inonu University Faculty of Medicine, Malatya, Turkey.en_US
dc.description.abstractPurpose: To create a new artificial intelligence approach based on deep learning (DL) from multiparametric MRI in the differential diagnosis of common parotid tumors. Methods: Parotid tumors were classified using the InceptionResNetV2 DL model and majority voting approach with MRI images of 123 patients. The study was conducted in three stages. At stage I, the classification of the control, pleomorphic adenoma, Warthin tumor and malignant tumor (MT) groups was examined, and two approaches in which MRI sequences were given in combined and non-combined forms were established. At stage II, the classification of the benign tumor, MT and control groups was made. At stage III, patients with a tumor in the parotid gland and those with a healthy parotid gland were classified. Results: A stage I, the accuracy value for classification in the non-combined and combined approaches was 86.43% and 92.86%, respectively. This value at stage II and stage III was found respectively as 92.14% and 99.29%. Conclusions: The approach presented in this study classifies parotid tumors automatically and with high accuracy using DL models.en_US
dc.identifier.doi10.1007/s00405-022-07455-y
dc.identifier.endpage11en_US
dc.identifier.pmid35596805
dc.identifier.scopus2-s2.0-85130296080en_US
dc.identifier.startpage1en_US
dc.identifier.urihttps://doi.org/10.1007/s00405-022-07455-y
dc.identifier.urihttps://hdl.handle.net/20.500.12899/1178
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofEuropean Archives of Oto-Rhino-Laryngologyen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectArtifcial ıntelligenceen_US
dc.subjectDeep learningen_US
dc.subjectParotid tumorsen_US
dc.subjectComputer aided diagnosisen_US
dc.subjectHead and neck canceren_US
dc.titleDeep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumorsen_US
dc.typeArticleen_US

Dosyalar

Orijinal paket
Listeleniyor 1 - 1 / 1
Küçük Resim
İsim:
Emrah Gündüz Makale Dosyası.pdf
Boyut:
1.27 MB
Biçim:
Adobe Portable Document Format
Açıklama:
Full Text / Tam Metin
İndir
Lisans paketi
Listeleniyor 1 - 1 / 1
Küçük Resim Yok
İsim:
license.txt
Boyut:
1.44 KB
Biçim:
Item-specific license agreed upon to submission
Açıklama:
İndir

Koleksiyon

Cerrahi Tıp Bilimleri Bölümü Koleksiyonu
Elektrik-Elektronik Mühendisliği Bölümü Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu
WoS İndeksli Yayınlar Koleksiyonu