3D CNN-based classification using sMRI and MD-DTI images for Alzheimer disease studies

TL;DR

This paper presents a 3D CNN approach that fuses sMRI and MD-DTI images for early Alzheimer's disease diagnosis, demonstrating improved classification performance and exploring data augmentation and ROI size effects.

Contribution

The study introduces a novel 3D CNN-based method combining sMRI and DTI modalities for AD classification, including new data augmentation and ROI size analysis techniques.

Findings

Fusion of sMRI and DTI improves classification accuracy.

Data augmentation helps balance class sizes effectively.

ROI size impacts the classification performance.

Abstract

Computer-aided early diagnosis of Alzheimers Disease (AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been the subject of extensive research in recent years. Some recent studies have shown promising results in the AD and MCI determination using structural and functional Magnetic Resonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) and Diffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imaging modalities in a supervised machine learning framework has shown promising direction of research. In this paper we first review major trends in automatic classification methods such as feature extraction based methods as well as deep learning approaches in medical image analysis applied to the field of Alzheimer's Disease diagnostics. Then we propose our own algorithm for Alzheimer's Disease diagnostics based on a convolutional neural network and sMRI and DTI modalities fusion on hippocampal ROI using data from the Alzheimers Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu). Comparison with a single modality approach shows promising results. We also propose our own method of data augmentation for balancing classes of different size and analyze the impact of the ROI size on the classification results as well.