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

Gunduz E., Alcin Ö. F., KIZILAY A., YILDIRIM İ. O.

EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY, cilt.279, sa.11, ss.5389-5399, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 279 Sayı: 11
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s00405-022-07455-y
  • Dergi Adı: EUROPEAN ARCHIVES OF OTO-RHINO-LARYNGOLOGY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, BIOSIS, CAB Abstracts, EMBASE, MEDLINE, Veterinary Science Database
  • Sayfa Sayıları: ss.5389-5399
  • Anahtar Kelimeler: Artificial intelligence, Deep learning, Parotid tumors, Computer aided diagnosis, Head and neck cancer, NEEDLE-ASPIRATION-CYTOLOGY, ACCURACY
  • İnönü Üniversitesi Adresli: Evet

Özet

Purpose 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.