Multimodal Neuroimaging based Alzheimer's Disease Diagnosis using Evolutionary RVFL Classifier

Abstract

Alzheimer's disease (AD) is one of the most known causes of dementia which can be characterized by continuous deterioration in the cognitive skills of elderly people. It is a non-reversible disorder that can only be cured if detected early, which is known as mild cognitive impairment (MCI). The most common biomarkers to diagnose AD are structural atrophy and accumulation of plaques and tangles, which can be detected using magnetic resonance imaging (MRI) and positron emission tomography (PET) scans. Therefore, the present paper proposes wavelet transform-based multimodality fusion of MRI and PET scans to incorporate structural and metabolic information for the early detection of this life-taking neurodegenerative disease. Further, the deep learning model, ResNet-50, extracts the fused images' features. The random vector functional link (RVFL) with only one hidden layer is used to classify the extracted features. The weights and biases of the original RVFL network are being optimized by using an evolutionary algorithm to get optimum accuracy. All the experiments and comparisons are performed over the publicly available Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to demonstrate the suggested algorithm's efficacy.