Abstract
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.
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Data availability statement
The data sets generated or analyzed during the current study are available from the corresponding author on reasonable request.
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EG: conception and design or analysis and interpretation of data and writing the article. OFA: drafting of the manuscript or revising it for important intellectual content and analysis via Artificial Intelligence knowledge. AK: supervision and final approval of the version to be published. IOY: evaluation and guidance of the radiological technical details and the creation of the data set from the MRI images in the study. All authors read and approved the final manuscript.
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Ethical approval for this study was obtained from the local ethics committee (2020/1038).
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Gunduz, E., Alçin, O.F., Kizilay, A. et al. Deep learning model developed by multiparametric MRI in differential diagnosis of parotid gland tumors. Eur Arch Otorhinolaryngol 279, 5389–5399 (2022). https://doi.org/10.1007/s00405-022-07455-y
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DOI: https://doi.org/10.1007/s00405-022-07455-y