Combining of Multiple Deep Networks via Ensemble Generalization Loss, Based on MRI Images, for Alzheimer's Disease Classification

This letter proposes a novel way of using an ensemble of multiple deep convolutional neural networks (DCNNs) for Alzheimer's disease classification, based on magnetic resonance imaging (MRI) images. To create this ensemble of DCNNs, we propose to combine the use of multiple MRI projections (as input) with that of different DCNN architectures to increase the deep ensemble diversity. In particular, to find the optimal fusion weights of the DCNN members, we designed a novel deep ensemble generalization loss, which accounts for interaction and cooperation during the optimal weight search. The optimization framework, equipped with our ensemble generalization loss, was formulated and solved using the sequential quadratic programming. Through this method, we achieved optimal DCNN fusion weights (i.e., a high generalization performance). The experimental results showed that our proposed DCNN ensemble outperforms current deep learning-based methods: it is able to produce state-of-the-art results on the Alzheimer's disease neuroimaging initiative (ADNI) dataset.