检索结果-内蒙古大学图书馆

Brain tumor segmentation and classification using optimized U-Net

IMAGING SCIENCE JOURNAL 2024年第2期72卷 204-219页

作者： Shiny, K., V Noorul Islam Ctr Higher Educ Dept Comp Sci & Engn Kanyakumari 629180 Tamil Nadu India

This paper presents an optimization-driven classifier for classifying the brain tumour considering MRI. Here, the pre-operative and post-operative MRI is subjected to pre-processing, which is performed using filtering and Region of Interest (RoI) extraction techniques. The pre-processed output is fed to segmentation wherein the U-Net model is adapted for generating the segments. Then, the extraction of histogram features is done and the classification of tumours is done by U-Net, which is trained using the proposed poor Bird Swarm Optimization algorithm (PRBSA). Here, PRBSA is the integration of the poor and rich optimization (PRO) algorithm and Bird Swarm algorithm (BSA). At last, the classified output is considered for pixel change detection, which is carried out using speeded-up robust features (SURF). The proposed PRBSA-based U-Net offered improved performance with the highest accuracy of 94%, highest sensitivity of 93.7%, and highest specificity of 94%.

关键词： Pixel change detection brain tumour segmentation magnetic resonance image U-Net speeded up robust features histogram features poor and rich algorithm Bird swarm algorithm

来源：评论

学校读者我要写书评

暂无评论

Brain tumor segmentation and classification using optimized U-Net

引用

The Journal of Photographic Science 2024年第2期72卷

作者： Shiny K.V Department of Computer Science and Engineering Noorul Islam Centre for Higher Education Kanyakumari India

ABSTRACTThis paper presents an optimization-driven classifier for classifying the brain tumour considering MRI. Here, the pre-operative and post-operative MRI is subjected to pre-processing, which is performed using filtering and Region of Interest (RoI) extraction techniques. The pre-processed output is fed to segmentation wherein the U-Net model is adapted for generating the segments. Then, the extraction of histogram features is done and the classification of tumours is done by U-Net, which is trained using the proposed poor Bird Swarm Optimization algorithm (PRBSA). Here, PRBSA is the integration of the poor and rich optimization (PRO) algorithm and Bird Swarm algorithm (BSA). At last, the classified output is considered for pixel change detection, which is carried out using speeded-up robust features (SURF). The proposed PRBSA-based U-Net offered improved performance with the highest accuracy of 94%, highest sensitivity of 93.7%, and highest specificity of 94%.

关键词： Pixel change detection brain tumour segmentation magnetic resonance image U-Net speeded up robust features histogram features poor and rich algorithm Bird swarm algorithm

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：