计算机与现代化Issue(7)：60-67,8.DOI:10.3969/j.issn.1006-2475.2026.07.009
基于多原型的自闭症分类
Autism Classification Based on Multiple Prototypes
段为国1
作者信息
- 1. 南京信息工程大学计算机学院,江苏 南京 210044
- 折叠
摘要
Abstract
Autism spectrum disorder(ASD)is a complex and highly heterogeneous neurodevelopmental disorder whose etiology and neurobiological mechanisms remain largely unknown.Currently,clinical diagnosis relies primarily on behavioral observation for assessment,but this method is subjective and lacks objective biomarkers.Recent advances in machine learning in medical imaging area have provided new impetus for ASD research.Resting-state functional Magnetic Resonance Imaging(rs-fMRI)measures changes in blood oxygen levels in the brain,reflecting brain activity during non-task activity and providing spatiotem-poral information for quantifying functional connectivity between brain regions.Using rs-fMRI data,this paper proposes a convo-lutional neural network model based on multi-prototype learning for the classification of ASD patients and normal controls.This model effectively extracts topological structural features of the functional connectivity network and embeds category information into the learned brain network representation through prototype learning.The experimental results show that the proposed prototype learning-based diagnostic method outperforms state-of-the-art methods in classification accuracy and robustness,with the classi-fication accuracy rate increasing by more than 5 percentage points.This method combines convolutional neural networks with proto-type learning algorithms,providing a more effective new approach for understanding the diagnosis of autism spectrum disorders.关键词
自闭症谱系障碍/静息态功能磁共振/多原型学习/卷积神经网络/拓扑结构Key words
autism spectrum disorder/rs-fMRI/multiple prototype learning/convolutional neural network/topological structure分类
信息技术与安全科学引用本文复制引用
段为国..基于多原型的自闭症分类[J].计算机与现代化,2026,(7):60-67,8.
