Pre-operative glioma grade prediction from multi-modal MR images based on an ensemble of deep learning models

Glioma is one of the most deadly types of cancer diseases. Accurate assessment of pre-operative grading of glioma for patients with this disease can lead to better patient management. While Biopsy is the most commonly used diagnostic technique in routine clinical applications of glioma grading, it has several disadvantages such as it is invasive and prone to tissue trauma. Consequently, automated pre-operative glioma grade prediction techniques based on MR images are recently getting attention, so noninvasive. However, most of the recently developed automated techniques are based on the handcrafted image features extracted from the manually segmented tumor regions in MRI, which is tedious, time-consuming, and inaccurate. In this paper, we presented a novel automated glioma grade prediction method based on an ensemble of deep learning models. The proposed ensemble method is introduced in two main steps. In the first step, a novel deep learning architecture using the feature extraction layers of a pre-trained ImageNet model as a backend was proposed, and subsequently trained separately using 2D images reconstructed from the multi-modal MR images in the axial, coronal and sagittal planes, resulting in multiple base learners. In the second step, the outputs of the base learners were combined using various fusing strategies, including averaging, voting, and classical machine learning techniques. Experimental results on the BraTS benchmark dataset demonstrate that the proposed ensemble learning approach achieved superior performance compared to state-of-the-art MRI-based glioma grade prediction methods. We believe that the method proposed in this study can be used as a supporting tool for neurologists and radiologists in the the precise diagnosis of glioma.