Work

Ensemble network using oblique coronal MRI for Alzheimer’s disease diagnosis

Year: 2025

Type: article

Abstract: Alzheimer’s disease (AD) is a primary degenerative brain disorder commonly found in the elderly, Mild cognitive impairment (MCI) can be considered a transitional stage from normal aging to Alzheimer’s disease. Therefore, distinguishing between normal aging and disease-induced neurofunctional impairments is crucial in clinical treatment. Although deep learning methods have been widely applied in Alzheimer’s diagnosis, the varying data formats used by different methods limited their clinical applicability. In this study, based on the ADNI dataset and previous clinical diagnostic experience, we propose a method using oblique coronal MRI to assist in diagnosis. We developed an algorithm to extract oblique coronal slices from 3D MRI data and used these slices to train classification networks. To achieve subject-wise classification based on 2D slices, rather than image-wise classification, we employed ensemble learning methods. This approach fused classification results from different modality images or different positions of the same modality images, constructing a more reliable ensemble classification model. The experiments introduced various decision fusion and feature fusion schemes, demonstrating the potential of oblique coronal MRI slices in assisting diagnosis. Notably, the weighted voting from decision fusion strategy trained on oblique coronal slices achieved accuracy rates of 97.5% for CN vs. AD, 100% for CN vs. MCI, and 94.83% for MCI vs. AD across the three classification tasks. • Extracted oblique coronal slice from 3D MRI to improve feature representation for AD. • Achieved subject-level classification using an ensemble of 2D deep learning models. • Proposed two ensemble methods, DPSM and SPDM, comparing their effectiveness. • Developed decision fusion and feature fusion techniques to enhance model performance.

Source: NeuroImage

DOI: 10.1016/j.neuroimage.2025.121151

Authors: Cunhao Li, Zhongjian Gao, Xiaomei Chen, Xuqiang Zheng, Xiaoman Zhang, Chih-Yang Lin

Institutions: Fujian Normal University, Sanming University, Fujian Provincial Hospital, National Central University

Language: English

Topic: Dementia and Cognitive Impairment Research

Subfield: Psychiatry and Mental health

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,540