Fusion of CNN and feature extraction methods for multiple sclerosis classification

Abstract

Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease that damages the central nervous system by causing small lesions in the brain. In this study, we present the fusion of four features extraction methods such as the 3D Local Binary Pattern (3D-LBP), 3D Decimal Descriptor Patterns (3D-DDP), Local Binary Pattern from Three Orthogonal Planes (LBP-TOP) and Decimal Descriptor Patterns from Three Orthogonal Planes (DDP-TOP) with Convolutional Neural Network (CNN) for MS classification using three 3D MRI sequences datasets T1, T2 and PD from 3D BrainWeb dataset. We implement twelve CNN models and apply each method with each of the CNN models on T1, T2 then PD MRI sequences. The experimental results demonstrate that 3D-DDP and DDP-TOP methods are the most robust and, for the contrast change effect of MRI sequences on the classification results, T2 yields the best performance.