Deep Multi-Scale 3D Convolutional Neural Network (CNN) for MRI Gliomas Brain Tumor Classification

Accurate and fully automatic brain tumor grading from volumetric 3D magnetic resonance imaging (MRI) is an essential procedure in the field of medical imaging analysis for full assistance of neuroradiology during clinical diagnosis. We propose, in this paper, an efficient and fully automatic deep multi-scale three-dimensional convolutional neural network (3D CNN) architecture for glioma brain tumor classification into low-grade gliomas (LGG) and high-grade gliomas (HGG) using the whole volumetric T1-Gado MRI sequence. Based on a 3D convolutional layer and a deep network, via small kernels, the proposed architecture has the potential to merge both the local and global contextual information with reduced weights. To overcome the data heterogeneity, we proposed a preprocessing technique based on intensity normalization and adaptive contrast enhancement of MRI data. Furthermore, for an effective training of such a deep 3D network, we used a data augmentation technique. The paper studied the impact of the proposed preprocessing and data augmentation on classification accuracy.