Classification of sMRI for Alzheimer's disease Diagnosis with CNN: Single Siamese Networks with 2D+? Approach and Fusion on ADNI

Abstract

The methods of Content-Based visual information indexing and retrieval penetrate into Healthcare and become popular in Computer-Aided Diagnostics. The PhD research we have started 13 months ago is devoted to the multimodal classification of MRI brain scans for Alzheimer Disease diagnostics. We use the winner classifier, such as CNN. We first proposed an original 2D+ approach. It avoids heavy volumetric computations and uses domain knowledge on Alzheimer biomarkers. We study discriminative power of different brain projections. Three binary classification tasks are considered separating Alzheimer Disease (AD) patients from Mild Cognitive Impairment (MCI) and Normal Control subject (NC). Two fusion methods on FC layer and on the single-projection CNN output show better performances, up to 91% of accuracy is achieved. The results are competitive with the SOA which uses heavier algorithmic chain.