AMIM: An Adaptive Weighted Multimodal Integration Model for Alzheimer’s Disease Classification

Abstract

Alzheimer's disease (AD) is an irreversible neurological disorder, so early medical diagnosis is extremely important. Magnetic resonance imaging (MRI) is one of the main medical imaging methods used clinically to detect and diagnose AD. However, most existing computer-aided diagnostic methods only use MRI slices for model architecture design. They ignore informational differences between all slices. In addition, physicians often use multimodal data, such as medical images and clinical information, to diagnose patients. The approach helps physicians to make more accurate judgments. Therefore, we propose an adaptive weighted multimodal integration model (AMIM) for AD classification. The model uses global information images, maximum information slices and clinical information as data inputs for the first time. It adopts adaptive weights integration method for classification. Experimental results show that our model achieves an accuracy of 99.00% for AD versus normal controls (NC), and 82.86% for mild cognitive impairment (MCI) versus NC. The proposed model achieves best classification performance in terms of accuracy, compared with most state-ofthe-art methods.