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混合深度学习模型用于自闭症谱系障碍诊断

D Aarthi¹, S Kannimuthu²

¹Department of Computer Science and Engineering, Karpagam College of Engineering, Coimbatore, 641032, India. aarthidevacse@gmail.com.

Scientific reports

|December 30, 2025

概括

这项研究引入了一种新的AI模型,用于使用面部图像在儿童中诊断自闭症谱系障碍 (ASD). 移动网络V2+GRU模型实现了高精度,提供了更快,更客观的诊断工具.

科学领域:

神经科学是一个神经科学.
计算机科学计算机科学
医疗成像医学成像

背景情况:

自闭症谱系障碍 (ASD) 是一种影响沟通和社会互动的神经发育状况.
目前的诊断方法是主观的,取决于临床医生,耗时,阻碍了早期检测.
客观和有效的诊断工具对于ASD儿童的及时干预至关重要.

研究的目的:

通过使用面部图像来评估五种混合深度学习模型用于ASD诊断的性能.
确定最有效的模型来准确和高效地预测儿童的自闭症.
解决传统主观诊断方法的局限性.

主要方法:

利用Kaggle的面部图像数据集来训练和测试深度学习模型.
评估了五种混合型号:MobileNetV2+BiLSTM,ResNet50+LSTM,EfficientNetB4,InceptionV3和MobileNetV2+GRU. 这三种混合型号均为该项目的首选.
优化每个模型在特定数据集上的性能.

主要成果:

移动NetV2+GRU混合型号表现出卓越的性能.
实现了95.5%的测试准确度,95.94%的精度和95.45%的F1分数.
性能优于其他模型,ROC值为98%,表明诊断能力高.

结论:

移动NetV2+GRU模型显示了客观和早期儿童自闭症诊断的巨大潜力.
拟议的模型提供了通过面部图像分析识别ASD的高性能和通用性.
这种人工智能驱动的方法可以帮助临床医生更快,更可靠地检测ASD.

关键词:

自闭症谱系障碍 (ASD) 是一种卷积神经网络 (CNN) 是一种神经网络.深度学习 (DL) 是指深度学习.门式经常性单位 (GRU) 网络.混合动力模型混合动力模型机器学习 (ML) 是指机器学习.

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