检索结果-内蒙古大学图书馆

Efficient feature fusion model withmodified bidirectional lstm for automatic Parkinson's disease classification

International Journal of Information Technology (Singapore) 2024年第6期16卷 3963-3971页

作者： Reshma, S. Chennakesavulu, M. Patil, Shweta Sidagounda Lamani, Manjunath Ramanna Dept of Artificial Intelligence and Machine Learning Dayananda Sagar College of Engineering Bangalore India Department of Electronics and Communication Engineering Rajeev Gandhi Memorial College of Engineering and Technology (Autonomous) Andhra Pradesh Nandyal 518501 India Rajarambapu Institute of Technology Rajaramnagar Uran Islampur India CHRIST(Deemed to Be University) Kanmanike Kumbalgudu Mysore Road Karnataka Bangalore 560074 India

The majority of people affected by Parkinson’s disease (PD) are middle-aged and older. The condition causes a variety of severe symptoms, including tremors, limited flexibility, and slow movements. As Parkinson’s disease develops with changing symptoms and growing severity, the importance of computer-aided diagnosis based on algorithms cannot be highlighted. Gait recognition technology appears to be a potential path for Parkinson's disease identification since it captures unique properties of a person’s walking pattern without requiring active participation, providing stability and non-intrusiveness. To begin, the median filter is used to remove noise from the input images received during data collection. This paper describes a new method for finding local and global features in gait images to assess the severity of Parkinson’s *** features are extracted using a stacked autoencoder, and global features are obtained using an Improved Convolutional Neural Network (ICNN). The Enhanced Sunflower Optimisation (ESO) technique is used to improve the CNN model's performance by optimizing hyperparameters such as batch size, learning rate, and number of convolutional layers. To classify PD severity, a modified bidirectional lstm (MBi-lstm) classifier receives input in the form of a combination of local and global features. The proposed model's performance is completely evaluated with the GAIT-IT and GAIT-IST datasets, which include key measures such as accuracy, precision, recall, and the F-measure. This study improves the diagnosis of Parkinson’s disease by introducing a non-intrusive real-time monitoring system capable of early detection and prevention. © Bharati Vidyapeeth's Institute of Computer Applications and Management 2024.

关键词： Enhanced sunflower optimisation Gait recognition GAIT-IT GAIT-ITS Improved convolutional neural network Median filter modified bidirectional lstm Parkinson’s disease

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：