Akademik Veri Yönetim Sistemi
European Journal of Therapeutics, cilt.31, sa.1, ss.44-50, 2025 (ESCI)
Objectives: It is aimed at magnetic resonance imaging (MRI)-based differential diagnosis of parotid gland tumors (PGTs) using deep learning. Methods: This study included 117 PGTs obtained from 113 patients. T2-w, T1-w, contrastenhanced T1-w, Diffusion Weighted Imaging-b0, Diffusion Weighted Imaging-b2000 (DWI-2000), and apparent diffusion coefficient sequences of these patients were used in the study. We implemented four different classification models, and we categorized the images as benign-malignant, pleomorphic adenoma (PA)-Warthin, Warthin-malignant, and all classes (mucoepidermoid carcinoma-other benign-other malignant-PA-Warthin). We constructed classification for each sequence separately using the ResNet18 architecture, with the dataset split into 80% for training and 20% for validation. Results: The most successful model in this study, achieving an accuracy of 95.37% and an F1-score of 94.74% in classifying malignant-Warthin images in T1-w sequences, also demonstrated the highest accuracy among all models evaluated. For the classification of benign-malignant and the differentiation across all classes, the highest accuracies were achieved with the T2-w sequence at 93.75% and 86.67%, respectively. In the differentiation of PA-Warthin, T1-w and DWI-b0 sequences demonstrated the highest performance, both with an accuracy of 90.36%. Conclusion: The deep networks proposed in the study supported MRI-based differential diagnosis of PGTs with high accuracy, and the user-friendly software classified images with high accuracy in about 10 seconds.