Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particula...Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.展开更多
自闭症谱系障碍是一种在儿童和青少年间常见的神经发育障碍,对于自闭症的诱发因素的研究已有几十年的历史,并且也出现了多种治疗和干预手段。这些方法大多是采用传统的行为干预或使用药物治疗等,存在着各自的优劣。随着脑成像技术的发展...自闭症谱系障碍是一种在儿童和青少年间常见的神经发育障碍,对于自闭症的诱发因素的研究已有几十年的历史,并且也出现了多种治疗和干预手段。这些方法大多是采用传统的行为干预或使用药物治疗等,存在着各自的优劣。随着脑成像技术的发展,研究者们可以进一步对人们的脑神经活动进行深入了解,并且以此为基础,发展出一种全新的干预技术——神经反馈训练。神经反馈技术可以直接收集个体的脑神经活动状态并以视觉和听觉的形式产生反馈,使个体可以直接对自己的脑活动产生调节。由于其非侵入型、无创性的优点,神经反馈技术越来越多被运用于心理疾病的治疗以及精神健康问题的改善中。尽管这种技术的出现和发展已有二十多年的历史,但是将其应用于ASD的干预和治疗中的实践仍是较少的,本文首先对ASD的传统和常用的干预手段简要说明,然后采用系统性文献综述和元分析方法(PRISMA),对Web of Science、PubMed、谷歌学术、中国知网、百度学术等数据库以“神经反馈”和“自闭症”等为关键词进行汇总和总结。对先前研究者们将这一技术应用于ASD的干预和治疗中的文章进行综述,总结这种技术在ASD的干预中的程序设计、训练方法等,本综述的主要结果显示,神经反馈技术在自闭症谱系障碍的干预和治疗中有显著效果,并且出现这种改善效果所需的时间周期较短,因此,这种无创性的技术可以在自闭症谱系障碍的治疗中得到更多的应用。此外,本研究对自闭症谱系障碍的脑功能连接也进行了一定程度的说明,因此提出未来研究可以通过对自闭症患者的脑功能连接以及神经网络为切入点,寻找进一步改善和优化这种技术的可能性。展开更多
文摘Autism Spectrum Disorder(ASD)is a neurodevelopmental condition characterized by significant challenges in social interaction,communication,and repetitive behaviors.Timely and precise ASD detection is crucial,particularly in regions with limited diagnostic resources like Pakistan.This study aims to conduct an extensive comparative analysis of various machine learning classifiers for ASD detection using facial images to identify an accurate and cost-effective solution tailored to the local context.The research involves experimentation with VGG16 and MobileNet models,exploring different batch sizes,optimizers,and learning rate schedulers.In addition,the“Orange”machine learning tool is employed to evaluate classifier performance and automated image processing capabilities are utilized within the tool.The findings unequivocally establish VGG16 as the most effective classifier with a 5-fold cross-validation approach.Specifically,VGG16,with a batch size of 2 and the Adam optimizer,trained for 100 epochs,achieves a remarkable validation accuracy of 99% and a testing accuracy of 87%.Furthermore,the model achieves an F1 score of 88%,precision of 85%,and recall of 90% on test images.To validate the practical applicability of the VGG16 model with 5-fold cross-validation,the study conducts further testing on a dataset sourced fromautism centers in Pakistan,resulting in an accuracy rate of 85%.This reaffirms the model’s suitability for real-world ASD detection.This research offers valuable insights into classifier performance,emphasizing the potential of machine learning to deliver precise and accessible ASD diagnoses via facial image analysis.
文摘自闭症谱系障碍是一种在儿童和青少年间常见的神经发育障碍,对于自闭症的诱发因素的研究已有几十年的历史,并且也出现了多种治疗和干预手段。这些方法大多是采用传统的行为干预或使用药物治疗等,存在着各自的优劣。随着脑成像技术的发展,研究者们可以进一步对人们的脑神经活动进行深入了解,并且以此为基础,发展出一种全新的干预技术——神经反馈训练。神经反馈技术可以直接收集个体的脑神经活动状态并以视觉和听觉的形式产生反馈,使个体可以直接对自己的脑活动产生调节。由于其非侵入型、无创性的优点,神经反馈技术越来越多被运用于心理疾病的治疗以及精神健康问题的改善中。尽管这种技术的出现和发展已有二十多年的历史,但是将其应用于ASD的干预和治疗中的实践仍是较少的,本文首先对ASD的传统和常用的干预手段简要说明,然后采用系统性文献综述和元分析方法(PRISMA),对Web of Science、PubMed、谷歌学术、中国知网、百度学术等数据库以“神经反馈”和“自闭症”等为关键词进行汇总和总结。对先前研究者们将这一技术应用于ASD的干预和治疗中的文章进行综述,总结这种技术在ASD的干预中的程序设计、训练方法等,本综述的主要结果显示,神经反馈技术在自闭症谱系障碍的干预和治疗中有显著效果,并且出现这种改善效果所需的时间周期较短,因此,这种无创性的技术可以在自闭症谱系障碍的治疗中得到更多的应用。此外,本研究对自闭症谱系障碍的脑功能连接也进行了一定程度的说明,因此提出未来研究可以通过对自闭症患者的脑功能连接以及神经网络为切入点,寻找进一步改善和优化这种技术的可能性。
