Automated detection of Alzheimer’s disease: a multi-modal approach with 3D MRI and amyloid PET

Author:

Castellano GiovannaORCID,Esposito AndreaORCID,Lella EufemiaORCID,Montanaro GrazianoORCID,Vessio GennaroORCID

Abstract

AbstractRecent advances in deep learning and imaging technologies have revolutionized automated medical image analysis, especially in diagnosing Alzheimer’s disease through neuroimaging. Despite the availability of various imaging modalities for the same patient, the development of multi-modal models leveraging these modalities remains underexplored. This paper addresses this gap by proposing and evaluating classification models using 2D and 3D MRI images and amyloid PET scans in uni-modal and multi-modal frameworks. Our findings demonstrate that models using volumetric data learn more effective representations than those using only 2D images. Furthermore, integrating multiple modalities enhances model performance over single-modality approaches significantly. We achieved state-of-the-art performance on the OASIS-3 cohort. Additionally, explainability analyses with Grad-CAM indicate that our model focuses on crucial AD-related regions for its predictions, underscoring its potential to aid in understanding the disease’s causes.

Publisher

Springer Science and Business Media LLC

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. From Voxels to Insights: Exploring the Effectiveness and Transparency of Graph Neural Networks in Brain Tumor Segmentation;2024 International Joint Conference on Neural Networks (IJCNN);2024-06-30

2. A Review on Alzheimer Disease Classification using different ML and DL Models;International Journal of Scientific Research in Computer Science, Engineering and Information Technology;2024-05-30

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