Keywords: Head & Neck/ENT, Machine Learning/Artificial Intelligence
Motivation: Preoperative distinguishment of the benign parotid gland tumors from the malignant determines the surgical scope; however, identifying tumor nature with only T1-weighted and fat-suppressed T2-weighted images is challenging.
Goal(s): Inserting three adjacent slices of the tumor into the RGB channels of a 2D image as 2.5D images coupled with transfer learning was utilized.
Approach: Using 2D and 2.5D images as input, a ResNet-101 model, pre-trained on ImageNet, was employed for transfer learning to facilitate the prediction.
Results: Deep learning models discerned malignant parotid gland tumors from the benign, especially 2.5D model showed superior performance to 2D model.
Impact: The transfer learning and 2.5D-MRI based classification model offered new insights to differentiate the malignant parotid gland tumors from the benign ones, especially when sample quantities are limited.
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