使用MEResNext检测自闭症谱系障碍的新型混合深度学习模型
1Department of Electronics Engineering, School of Engineering and Technology, Vivekananda Institute of Professional Studies-Technical Campus (VIPS-TC), AU-Block_(Outer Ring Road) PitamPura, New Delhi 110034, India.
Computational biology and chemistry
|August 20, 2025
概括
这项研究引入了一种用于自闭症谱系障碍 (ASD) 检测的新型混合深度学习方法. MEResNeXt模型实现了高精度,灵敏度和特异性,为早期自闭症诊断提供了有前途的工具.
科学领域:
- 神经学
- 人工智能
- 医疗诊断
背景情况:
- 自闭症谱系障碍 (ASD) 是一种神经疾病,其特点是社会互动,沟通和重复行为.
- 早期发现和干预对于治疗自闭症至关重要,
- 及时诊断自闭症可以减轻长期影响和减轻症状的严重程度.
研究的目的:
- 开发和评估用于准确检测自闭症谱系障碍 (ASD) 的混合深度学习方法.
- 通过先进的特征选择和分类技术,改进现有的自闭症诊断模型.
主要方法:
- 一种混合深度学习方法,涉及三个阶段:预处理,特征选择和自闭症检测.
- 数据预处理使用Yeo-Jhonson转换来消除噪音和文物.
- 使用双指数光滑-鹿群优化器 (DeSEHO) 进行特征选择,将双指数光滑 (DES) 与鹿群优化器 (EHO) 集成.
- 使用Moments Embedding ResNeXt (MEResNeXt) 进行了ASD检测,这是Moments Embedding Network (MoNet) 和ResNeXt的融合.
主要成果:
- 与传统模型相比, 拟议的MEResNeXt模型表现出更高的性能.
- 在ASD检测中达到95. 3%的准确性,96. 5%的敏感性和94. 8%的特异性.
- 混合深度学习方法有效地确定了自闭症分类的关键特征.
结论:
- MEResNeXt模型代表了自闭症谱系障碍检测的深度学习的重大进步.
- 这项研究强调了混合人工智能方法在精确有效的神经疾病诊断方面的潜力.
- 通过使用先进的计算方法, 及早准确地检测ASD可以带来更好的患者结果.
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