检索结果-内蒙古大学图书馆

A dense kernel point convolutional neural network for chronic liver disease classification with hybrid chaotic slime mould and giant trevally optimizer

引用

BIOMEDICAL SIGNAL PROCESSING AND CONTROL 2025年 102卷

作者： Saranya, R. Jaichandran, R. Vinayaka Missions Res Fdn Deemed Be Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor 603104 Tamil Nadu India Vinayaka Missions Res Fdn Deemed Be Univ Aarupadai Veedu Inst Technol Dept Comp Sci & Engn Paiyanoor 603104 Tamil Nadu India

Chronic liver disease affects liver tissues and can lead to liver failure. Early diagnosis is crucial for providing better treatment and reducing the mortality rate. Traditional methods like biopsy and manual analysis of liver computed tomography scan images are commonly used for diagnosis. However, biopsy is invasive and can cause pain and other complications, while manual analysis by a radiologist is time-consuming and requires expert knowledge. This manuscript presents the dense kernel point convolutional neural network for Chronic Liver Disease Classification with Hybrid Chaotic Slime Mould and Giant Trevally Optimizer (DKPCNN-CLDC-HybCSMGTO) to improve diagnostic performance. For image segmentation, Fuzzy C-Ordered Means Clustering (IFCMC) method is applied, and for feature extraction, the Intuitionistic Invariant Wavelet Scattering Transform (IWST) is employed. The DKPCNN-CLDC-HybCSM-GTO method is compared with existing methods, including Preoperative Classification of Primary and Metastatic Chronic Liver Disease via Machine Learning-Based Ultrasound Radiomics (PC-PM-LC-MLUR), the Liver Disease Classification from Ultrasound Using Multi-Scale CNN (LDC-USMS), and the convolutional neural network for Classifying Primary Chronic Liver Disease Based on Triple-Phase CT and Tumor Marker Information (CNN-CPLC-TPCT-TMI). The evaluation metrics include accuracy, precision, sensitivity, specificity, and F1 score. The DKPCNN-CLDC-HybCSM-GTO approach shows significant improvements over existing methods: 22.36%, 25.42%, and 18.27% higher accuracy;22.36%, 15.42%, and 18.27% higher sensitivity;and 21.36%, 16.42%, and 19.27% higher specificity, respectively. These results demonstrate the method being proposed offers a more a valuable tool for early detection and better treatment outcomes in chronic liver disease.

关键词： dense kernel point convolutional neural network Hybrid chaotic slime mould and giant trevally optimizer Intuitionistic fuzzy C-ordered means clustering Invariant wavelet scattering transforms

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：