Multiple Inputs and Mixed Data for Alzheimer’s Disease Classification Based on 3D Vision Transformer

Juan A. Castro-Silva (), María N. Moreno-García and Diego H. Peluffo-Ordóñez
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Juan A. Castro-Silva: Data Mining (MIDA) Research Group, Universidad de Salamanca, 37007 Salamanca, Spain
María N. Moreno-García: Data Mining (MIDA) Research Group, Universidad de Salamanca, 37007 Salamanca, Spain
Diego H. Peluffo-Ordóñez: Faculty of Engineering, Corporación Universitaria Autónoma de Nariño, Pasto 520001, Colombia

Mathematics, 2024, vol. 12, issue 17, 1-13

Abstract: The current methods for diagnosing Alzheimer’s Disease using Magnetic Resonance Imaging (MRI) have significant limitations. Many previous studies used 2D Transformers to analyze individual brain slices independently, potentially losing critical 3D contextual information. Region of interest-based models often focus on only a few brain regions despite Alzheimer’s affecting multiple areas. Additionally, most classification models rely on a single test, whereas diagnosing Alzheimer’s requires a multifaceted approach integrating diverse data sources for a more accurate assessment. This study introduces a novel methodology called the Multiple Inputs and Mixed Data 3D Vision Transformer (MIMD-3DVT). This method processes consecutive slices together to capture the feature dimensions and spatial information, fuses multiple 3D ROI imaging data inputs, and integrates mixed data from demographic factors, cognitive assessments, and brain imaging. The proposed methodology was experimentally evaluated using a combined dataset that included the Alzheimer’s Disease Neuroimaging Initiative (ADNI), the Australian Imaging, Biomarker, and Lifestyle Flagship Study of Ageing (AIBL), and the Open Access Series of Imaging Studies (OASIS). Our MIMD-3DVT, utilizing single or multiple ROIs, achieved an accuracy of 97.14%, outperforming the state-of-the-art methods in distinguishing between Normal Cognition and Alzheimer’s Disease.

Keywords: Alzheimer’s disease; region of interest; multiple inputs; mixed data; 3D vision transformer (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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