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A plethora of deep learning models have been developed for the task ofAlzheimer's disease classification from brain MRI scans. Many of these modelsreport high performance, achieving three-class classification accuracy of up to95%. However, it is common for these studies to draw performance comparisonsbetween models that are trained on different subsets of a dataset or usevarying imaging preprocessing techniques, making it difficult to objectivelyassess model performance. Furthermore, many of these works do not providedetails such as hyperparameters, the specific MRI scans used, or their sourcecode, making it difficult to replicate their experiments. To address theseconcerns, we present a comprehensive study of some of the deep learning methodsand architectures on the full set of images available from ADNI. We find that,(1) classification using 3D models gives an improvement of 1% in our setup, atthe cost of significantly longer training time and more computation power, (2)with our dataset, pre-training yields minimal ($<0.5\%$) improvement in modelperformance, (3) most popular convolutional neural network models yield similarperformance when compared to each other. Lastly, we briefly compare the effectsof two image preprocessing programs: FreeSurfer and Clinica, and find that thespatially normalized and segmented outputs from Clinica increased the accuracyof model prediction from 63% to 89% when compared to FreeSurfer images.

A Comprehensive Study of Alzheimer's Disease Classification Using Convolutional Neural Networks