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基于自适应网络的模糊推理系统efficientnet用于自闭症谱系障碍检测,以优化为基础的枢纽区域提取

Satish Muppidi¹, Kishore Bhamidipati², Grandhi Siva Sankar³

¹Department of Information Technology, GMR Institute of Technology, Rajam, India.

Psychiatry research. Neuroimaging

|December 24, 2025

概括

一种新的混合模型,即自适应网络基于模糊推理系统EfficientNet (ANFIS-EffNet),使用脑成像有效检测自闭症谱系障碍 (ASD). 这种方法为诊断自闭症提供了更准确,更有效的方法,改进了传统的技术.

科学领域:

神经科学是一个神经科学.
人工智能的人工智能
医疗成像医学成像

背景情况:

自闭症谱系障碍 (ASD) 与神经活动异常和早期大脑发育有关.
传统的ASD诊断方法耗时,结果不清楚,影响社会和沟通技巧.
需要对ASD进行先进,高效和准确的诊断工具.

研究的目的:

开发和评估一种新的混合方法来检测自闭症谱系障碍 (ASD).
通过神经成像数据提高ASD诊断的准确性和效率.
将自适应网络基于模糊推理 (ANFIS) 和EfficientNet结合起来,以加强ASD识别.

主要方法:

通过修改ANFIS和EfficientNet层,开发了一种混合模型,即基于自适应网络的模糊推理系统EfficientNet (ANFIS-EffNet).
图像的预处理涉及到异型扩散和感兴趣区域 (ROI) 提取.
基于功能连接的关键区域提取使用了亚当战争战略优化 (AWSO),结合了亚当优化和战争战略优化 (WSO).
特征提取包括学习不变特征转换 (LIFT) 和统计方法.

主要成果:

该ANFIS-EffNet模型在ASD检测方面表现出很高的性能.
获得了93.219%的准确性.
获得了93.670%的灵敏度和93.840%的特异性.

结论:

ANFIS-EffNet模型为自闭症谱系障碍的检测提供了一种高度有效和准确的方法.
这种混合方法在准确性和效率方面明显优于传统方法.
开发的模型显示了改善ASD早期诊断和干预的希望.

关键词:

自闭症是一种自闭症疾病障碍.深度学习是一种深度学习.核心区域提取的重要区域提取战争战略优化战争战略优化

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