Multimodal Fusion-Based Deep Learning Network for Effective Diagnosis of Alzheimer’s Disease

Abstract

Alzheimer’s disease (AD) is a prevalent, irreversible, chronic, and degenerative disorder whose diagnosis at the prodromal stage is critical. Mostly, single modality data, such as magnetic resonance imaging (MRI) or positron emission tomography (PET), are used to make predictions in AD studies. However, the metabolic and structural data fusion can provide a holistic view of AD-staging analysis. To achieve this objective, a novel multimodal fusion-based method is proposed in this article. An optimal fusion of MRI and PET is achieved by harnessing demon algorithm and discrete wavelet transform. Finally, the fused image features are extracted using ResNet-50, and these features are classified using robust energy least square twin support vector machine classifier. Experiments on the AD neuroimaging initiative dataset show descent accuracy of 97%, 94%, and 97.5% for cognitive normal (CN) versus AD, CN versus mild cognitive impairment (MCI), and AD versus MCI, respectively. The proposed model will be beneficial for health professionals in accurately diagnosing AD at an early stage.