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ASD-SWNet:一种新的共享重量特征提取和分类网络,用于自闭症谱系障碍诊断

Jian Zhang¹, Jifeng Guo², Donglei Lu³

¹School of Internet of Things and Artificial Intelligence, Wuxi Vocational College of Science and Technology, Wuxi, 214028, China. nulishangan123@gmail.com.

Scientific reports

|June 13, 2024

概括

这项研究介绍了ASD-SWNet,这是一个用于使用功能性MRI诊断自闭症谱系障碍 (ASD) 的新型网络. 它通过整合无监督和监督学习来提高诊断准确度,以更好地检测自闭症.

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:

目前的自闭症谱系障碍 (ASD) 诊断依赖于主观方法,影响治疗和生活质量.
准确和早期的ASD诊断对于有效的干预和患者的结果至关重要.
为ASD开发客观和精确的诊断工具是一个重大挑战.

研究的目的:

提出ASD-SWNet,一个新的共享权重网络,用于增强自闭症谱系障碍 (ASD) 诊断.
改善无监督和监督学习的整合,以更精确地检测ASD.
利用功能磁共振成像 (fMRI) 进行客观和强大的ASD诊断.

主要方法:

ASD-SWNet使用具有高斯噪声的自动编码器 (AE) 来从fMRI数据中进行强大的特征提取.
一个定制的卷积神经网络 (CNN) 执行分类,其权重由AE学习的特征初始化.
一个共享权重机制使AE和CNN的联合训练成为可能,增强了功能集成.
一个新的数据增强策略解决了有限时间序列医疗数据的挑战.

主要成果:

拟议的方法在ABIDE-I数据集上实现了76.52%的诊断准确率和0.81的曲线下面积 (AUC).
嵌套十倍交叉验证证明了ASD-SWNet的稳定性和高性能.
该方法在诊断准确性和ASD检测可靠性方面远远超过现有方法.

结论:

ASD-SWNet提供了使用fMRI诊断自闭症谱系障碍 (ASD) 的更精确,更强大的方法.
该研究强调了将无监督和监督学习整合到神经系统疾病诊断中的潜力.
拟议的框架为开发其他神经性脑疾病的诊断工具提供了基础.

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