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In the last decade, computer-aided early diagnostics of Alzheimer's Disease(AD) and its prodromal form, Mild Cognitive Impairment (MCI), has been thesubject of extensive research. Some recent studies have shown promising resultsin the AD and MCI determination using structural and functional MagneticResonance Imaging (sMRI, fMRI), Positron Emission Tomography (PET) andDiffusion Tensor Imaging (DTI) modalities. Furthermore, fusion of imagingmodalities in a supervised machine learning framework has shown promisingdirection of research.  In this paper we first review major trends in automatic classificationmethods such as feature extraction based methods as well as deep learningapproaches in medical image analysis applied to the field of Alzheimer'sDisease diagnostics. Then we propose our own design of a 3D Inception-basedConvolutional Neural Network (CNN) for Alzheimer's Disease diagnostics. Thenetwork is designed with an emphasis on the interior resource utilization anduses sMRI and DTI modalities fusion on hippocampal ROI. The comparison with theconventional AlexNet-based network using data from the Alzheimer's DiseaseNeuroimaging Initiative (ADNI) dataset (http://adni.loni.usc.edu) demonstratessignificantly better performance of the proposed 3D Inception-based CNN.

3D Inception-based CNN with sMRI and MD-DTI data fusion for Alzheimer's Disease diagnostics