Feature level fusion framework for brain MR image classification using supervised deep learning and hand crafted features

Abstract EN

In this paper, we propose an efficient fusion framework for brain magnetic resonance (MR) image classification using deep learning and handcrafted feature extraction methods; namely, histogram of oriented gradients (HOG) and local binary patterns (LBPs).

The proposed framework aims to: (1) determine the optimal handcrafted features by Genetic Algorithm (GA) (2) discover the fully connected (FC) layers’ features using fine-tuned convolutional neural network (CNN) (3) employ the canonical correlation analysis (CCA) and the discriminant correlation analysis (DCA) methods in feature-level fusion.

Extensive experiments were conducted and the classification performance was demonstrated on three benchmark datasets; viz., RD-DB1, TCIA-IXI-DB2 and TWB-HM-DB3.

Mean accuracy of 68.69%, 90.35% and 93.15% from CCA and 77.22%, 100.00% and 99.40% from DCA was achieved by the Support Vector Machines (SVM) sigmoid kernel classifier on RD-DB1, TCIA-IXI-DB2 and TWB-HM-DB3, respectively.

The obtained results of the proposed framework outperform when compared with other state-of-the-art works.