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

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dc.authoridKIZILAY, Ahmet/0000-0003-3048-6489
dc.authoridALCIN, Omer/0000-0002-2917-3736
dc.authoridGÜNDÜZ, EMRAH/0000-0001-8857-7290
dc.authorwosidKIZILAY, Ahmet/ABI-8293-2020
dc.authorwosidYıldırım, İsmail Okan/AFR-8243-2022
dc.authorwosidALCIN, Omer/AAH-3525-2020
dc.authorwosidGÜNDÜZ, EMRAH/AAA-4350-2021
dc.contributor.authorGunduz, Emrah
dc.contributor.authorAlcin, Omer Faruk
dc.contributor.authorKizilay, Ahmet
dc.contributor.authorYildirim, Ismail Okan
dc.date.accessioned2024-08-04T20:51:57Z
dc.date.available2024-08-04T20:51:57Z
dc.date.issued2022
dc.departmentİnönü Üniversitesien_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.endpage5399en_US
dc.identifier.issn0937-4477
dc.identifier.issn1434-4726
dc.identifier.issue11en_US
dc.identifier.pmid35596805en_US
dc.identifier.scopus2-s2.0-85130296080en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage5389en_US
dc.identifier.urihttps://doi.org/10.1007/s00405-022-07455-y
dc.identifier.urihttps://hdl.handle.net/11616/100659
dc.identifier.volume279en_US
dc.identifier.wosWOS:000799649300001en_US
dc.identifier.wosqualityQ2en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_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.subjectArtificial intelligenceen_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

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