A multiview-slice feature fusion network for early diagnosis of Alzheimer’s disease with structural MRI images

Abstract

Alzheimer’s disease (AD) is a prevalent neurodegenerative disorder with high incidence and significant mortality among the elderly worldwide. Nevertheless, early and accurate diagnosis and treatment of the disease could delay its progression to evolve into more severe phases. Traditional methods, which are largely binary in classification, often struggle with the complexity of multi-classification tasks, resulting in lower accuracy rates. To address this, we propose a multiview-slice feature fusion network that uses structural magnetic resonance imaging (MRI) images to accurately distinguish between normal control (NC), mild cognitive impairment (MCI), and AD subjects. The scientific contribution of this study resides in the innovative integration of a multi-view scheme with lightweight networks to design sophisticated modules, significantly enhancing the accuracy of early AD multi-classification. Firstly, an improved EfficientNet model is designed using the DropBlock technique, i.e., DB-EfficientNet, which can be incorporated with MobileViT and ShuffleNet V2 models to develop a novel hybrid deep feature extraction approach for extracting the features of multi-view slices. In addition, a feature enhancement and aggregation module is proposed based on the original hybrid attention (HBA) mechanism to strengthen the global representation of multi-view slices by capturing the dependencies among long-distance features. Meanwhile, a feature fusion strategy is formulated to efficiently fuse the refined features to further improve the accuracy of the model. To validate the performance of the proposed network for AD diagnosis, a range of experiments were implemented using the public Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The proposed scheme yielded average accuracies of 97.64%, 96.47%, 96.48%, and 95.31% on the ADNI-1 dataset as well as 97.71%, 97.48%, 96.30%, and 96.14% on the ADNI-2 dataset for AD/NC, MCI/NC, AD/MCI, and AD/MCI/NC classifications, respectively. These results have demonstrated that our scheme outperforms recent comparable methods, thus offering a significant reference for early and accurate AD diagnosis.