Research on Alzheimer’s Disease Brain MRI Classification Based on Broad Learnin

JIANG Jie; GUO Jin-xing; CAI Ying; FU Dan

doi:10.12263/newIT.2025.03.001

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Research on Alzheimer’s Disease Brain MRI Classification Based on Broad Learnin

RESEARCH PAPER | 更新时间：2025-12-01

- Research on Alzheimer’s Disease Brain MRI Classification Based on Broad Learnin
- New Generation of Information Technology Vol. 8, Issue 3, Pages: 1-5(2025)
- 作者机构：
  
  1.牡丹江林业医院感染科，黑龙江牡丹江 157011
  2.牡丹江医科大学附属红旗医院泌尿外科，黑龙江牡丹江 157011
  3.牡丹江医科大学附属红旗医院重症医学科，黑龙江牡丹江 157011
- 作者简介：
- 基金信息：
- DOI：10.12263/newIT.2025.03.001
  CLC： TP181
- Revised：2025-04-29，
  
  Accepted：30 April 2025，
  
  Published：15 March 2025
- 稿件说明：
移动端阅览
江洁, 郭金兴, 才莹, 等. 基于宽度学习的阿尔茨海默病脑部MRI分类研究[J]. 新一代信息技术, 2025, 8(3): 01-05

JIANG Jie, GUO Jin-xing, CAI Ying, et al. Research on Alzheimer’s Disease Brain MRI Classification Based on Broad Learnin[J]. New Generation of Information Technology, 2025, 8(3): 01-05
江洁, 郭金兴, 才莹, 等. 基于宽度学习的阿尔茨海默病脑部MRI分类研究[J]. 新一代信息技术, 2025, 8(3): 01-05 DOI： 10.12263/newIT.2025.03.001.

JIANG Jie, GUO Jin-xing, CAI Ying, et al. Research on Alzheimer’s Disease Brain MRI Classification Based on Broad Learnin[J]. New Generation of Information Technology, 2025, 8(3): 01-05 DOI： 10.12263/newIT.2025.03.001.

摘要

本研究使用一种新型的级联金字塔宽度学习系统（Cascade Pyramid Broad Learning System，CPBLS）以提升阿尔茨海默病（Alzheimer’s Disease，AD）的早期诊断效率。该系统整合了k-means聚类模块，用于优化特征提取和降维过程。通过功能性磁共振成像（Magnetic Resonance Imaging，MRI）数据，CPBLS模型在分类任务中展现了卓越的性能，包括准确率、精确率、召回率、

得分以及AUC值。宽度学习技术的应用，结合k-means模块，显著降低了模型的复杂度，并缩短了训练时间，为AD的早期诊断提供了快速且有效的解决方案。

Abstract

This study employs a novel cascade pyramid broad learning system (CPBLS) to enhance the early diagnosis efficiency of Alzheimer’s disease (AD). The system integrates a k-means clustering module to optimize feature extraction and dimensionality reduction.

Using functional magnetic resonance imaging (MRI) data

the CPBLS model demonstrates outstanding performance in classification tasks

including high accuracy

precision

recall

-score

and AUC values. The application of broad learning technology

combined with the k-means module

significantly reduces model complexity and shortens training time

providing a fast and effective solution for the early diagnosis of AD.

关键词

Keywords

references

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