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Attempt to diagnose Alzheimer's disease (AD) using imaging modalities is one of the scopes of deep learning. While considering the theoretical background from past studies, we are trying to identify convolutional neural network (CNN) behaviors moving from 2D to 3D architecture. This study aims to explore the output from a variety of CNN models implemented in the MRI or/and PET classification tasks for AD prediction while trying to summarize its characteristics with a variety of parameters that are tuned and changed. There are many architectures available; however, we are testing a basic architecture with a change in the reception area based on the convolutional layer's kernel size and its strides. The architecture has been categorized as converging, diverging, or equivalent if the filter kernel size is unchanged. This investigation studies a simple encoder based CNN with a sequential flow of features from low-level to high-level feature extraction. The idea is to present a diverging reception area by increasing the filter size and stride from a lower to a higher level. As a result, the feature redundancy is reduced and the trivial features keep on diminishing. The proposed architecture is referred to as `divNet', and several experiments were performed to determine how effective the architecture is in terms of the consumed memory, the number of parameters, running time, classification error, and the generalization error. This study surveys several related experiments by changing the hyper-parameters setting, the architecture selection based on the depth and area of the reception feature, and the data size.

3D CNN Design for the Classification of Alzheimer’s Disease Using Brain MRI and PET