Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks

Abstract--Automated methods for Alzheimer's disease (AD) classification have the potential for great clinical benefits and may provide insight for combating the disease. Machine learning, and more specifically deep neural networks, have been shown to have great efficacy in this domain. These algorithms often use neurological imaging data such as MRI and PET, but a comprehensive and balanced comparison of these modalities has not been performed. In order to accurately determine the relative strength of each imaging variant, this work performs a comparison study in the context of Alzheimer's dementia classification using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Furthermore, this work analyzes the benefits of using both modalities in a fusion setting and discusses how these data types may be leveraged in future AD studies using deep learning. Index Terms--Alzheimer's disease, computer aided diagnosis, convolutional neural network, multimodal, data fusion. LZHEIMER'S disease (AD) is a neurodegenerative disorder characterized by cognitive decline and dementia. The number of individuals living with AD in the United States is expected to reach 10 million by the year 2025 [1]. As a result, automated methods for computer aided diagnosis could greatly improve the ability to screen at-risk individuals. Such methods typically take as input patient data including demographics, medical history, genetic sequencing, and neurological images among others. The resulting output is health status indicated by a diagnosis label, which may also include a probabilistic uncertainty on the prediction.