Neuroimaging Modality Fusion in Alzheimer's Classification Using Convolutional Neural Networks
This work addresses the need for a balanced comparison of neuroimaging modalities in Alzheimer's classification, though it is incremental as it builds on existing deep learning methods without introducing new paradigms.
This study compared MRI and PET imaging modalities for Alzheimer's disease classification using the ADNI dataset and analyzed their fusion benefits, achieving competitive results but without specifying concrete performance numbers.
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) dataset. 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.