Classification of Benign and Malignant Parotid Gland Tumors using Deep Learning and 2.5D MRI

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.