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基于面部图像 (用于残疾诊断) 的强大的自闭症谱系障碍查:一个域自适应的深层合奏方法

Mohammad Shafiul Alam^1,2, Muhammad Mahbubur Rashid¹, Ahmad Jazlan¹

¹Department of Mechatronics Engineering, International Islamic University Malaysia, Kuala Lumpur 50728, Malaysia.

Diagnostics (Basel, Switzerland)

|July 12, 2025

概括

一个新的深度集体学习系统,ASD-UANet,使用面部图像准确地分类自闭症谱系障碍 (ASD). 这种人工智能方法显示出高精度和通用性,为早期ASD检测提供了一个有前途的工具.

科学领域:

医疗保健中的人工智能
深度学习用于医学诊断.
技术包括残疾人技术.

背景情况:

人工智能 (AI) 正在改变残疾人的医疗保健,包括自闭症谱系障碍 (ASD) 患者.
来自不同来源的不一致的数据在开发可靠的AI诊断工具方面构成了重大挑战.
使用面部图像准确可靠地对ASD进行分类,需要强大的深度学习方法.

研究的目的:

开发和评估一个深度集体学习系统,以从面部图像中准确地分类ASD.
通过整合多个公共数据集来解决数据不一致的问题.
评估开发系统在未见的实时数据上的通用性.

主要方法:

利用了两个公共的ASD面部图像数据集 (Kaggle和YTUIA),具有不同的人口统计和图像特征.
通过使用加权组合策略 (FPPR) 结合Xception和ResNet50V2架构开发了ASD-UANet组合模型.
评估了根据年龄和性别分层的组合数据集的模型性能,并在未见实时数据集 (UIFID) 上测试了概括性.

主要成果:

在综合数据集 (T1+T2) 上,ASD-UANet组合实现了96.0%的准确性和0.990的AUC,优于单个模型的表现.
在未见实时数据集 (T3) 上表现出强大的概括性,准确率为90.6%,AUC为0.930.
显著优于单个转移学习模型的表现,例如Xception单独 (83%的T1+T2准确率).

结论:

开发的ASD-UANet系统显示了公平和临床有益的ASD查的巨大潜力.
这种非侵入性,具有成本效益的方法为更精确的诊断提供了基础,并改善了ASD患者的包容性.
整合多种数据源和集体深度学习模型可以提高ASD的诊断准确性和可靠性.

关键词:

自闭症谱系障碍自闭症谱系障碍深度学习是一种深度学习.域名适应域名适应组合学习组合学习面部图像数据集面部图像数据集

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