Integrating convolutional neural networks, kNN, and Bayesian optimization for efficient diagnosis of Alzheimer's disease in magnetic resonance images

Deep learning is attracting growing interest from biomedical engineering community. Researchers and clinicians are also increasingly interested in development of machine learning and pattern recognition systems used to diagnose Alzheimer's disease (AD). To enhance diagnostic power for AD, we propose an automatic system integrating convolutional neural networks (CNN) to extract deep traits from magnetic resonance image (MRI) with no prior assumption, a filtering technique to reduce number of features, and k nearest neighbors (kNN) algorithm to discriminate AD subjects from healthy control (HC) ones. The kNN is tuned by Bayesian optimization (BO) algorithm. The experimental outcomes support the hypothesis that our proposed integrative system can be effective at performing MRI classification: 94.96% ± 0.0486 accuracy, 92.05% ± 0.0746 sensitivity, and 96.62% ± 0.0350 specificity. The obtained result underscore the utility of the proposed system for screening AD as it improves accuracy compared to existing models validated on the same data set.