检索结果-南通市图书馆

Deep Learning Based Classification of Wrist Cracks from X-ray Imaging

Computers, Materials & Continua 2022年第10期73卷 1827-1844页

作者： Jahangir Jabbar Muzammil Hussain hassaan malik Abdullah Gani Ali Haider Khan Muhammad Shiraz Department of Computer Science National College of Business Administration&Economics LahoreMultan60000Pakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Faculty of Computing and Informatics University of Malaysia SabahJalan UMSKota Kinabalu88400SabahMalaysia Department of Computer Science Federal Urdu University of ArtsScience and TechnologyIslamabadPakistan

Wrist cracks are the most common sort of cracks with an excessive occurrence *** the routine detection of wrist cracks,conventional radiography(X-ray medical imaging)is used but periodically issues are presented by crack *** cracks often appear in the human arbitrary bone due to accidental injuries such as ***,many hospitals lack experienced clinicians to diagnose wrist ***,an automated system is required to reduce the burden on clinicians and identify *** this study,we have designed a novel residual network-based convolutional neural network(CNN)for the crack detection of the *** the classification of wrist cracks medical imaging,the diagnostics accuracy of the RN-21CNN model is compared with four well-known transfer learning(TL)models such as Inception V3,Vgg16,ResNet-50,and Vgg19,to assist the medical imaging technologist in identifying the cracks that occur due to wrist *** RN-21CNN model achieved an accuracy of 0.97 which is much better than its competitor`s *** results reveal that implementing a correct generalization that a computer-aided recognition system precisely designed for the assistance of clinician would limit the number of incorrect diagnoses and also saves a lot of time.

关键词： Wrist cracks fracture deep learning X-rays CNN

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

Spatial Correlation Module for Classification of Multi-Label Ocular Diseases Using Color Fundus Images

引用

Computers, Materials & Continua 2023年第7期76卷 133-150页

作者： Ali Haider Khan hassaan malik Wajeeha Khalil Sayyid Kamran Hussain Tayyaba Anees Muzammil Hussain Department of Software Engineering Faculty of Computer SciencesLahore Garrison UniversityLahorePakistan Department of Computer Science School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan Department of Computer Science&Information Technology University of Engineering and Technology(UET)PeshawarPakistan Department of Computer Science TIMES InstituteMultan60000Pakistan Department of Software Engineering School of Systems and TechnologyUniversity of Management and TechnologyLahore54000Pakistan

To prevent irreversible damage to one’s eyesight,ocular diseases(ODs)need to be recognized and treated *** fundus imaging(CFI)is a screening technology that is both effective and *** to CFIs,the early stages of the disease are characterized by a paucity of observable symptoms,which necessitates the prompt creation of automated and robust diagnostic *** traditional research focuses on image-level diagnostics that attend to the left and right eyes in isolation without making use of pertinent correlation data between the two sets of *** addition,they usually only target one or a few different kinds of eye diseases at the same *** this study,we design a patient-level multi-label OD(PLML_ODs)classification model that is based on a spatial correlation network(SCNet).This model takes into consideration the relevance of patient-level diagnosis combining bilateral eyes and multi-label ODs ***_ODs is made up of three parts:a backbone convolutional neural network(CNN)for feature extraction i.e.,DenseNet-169,a SCNet for feature correlation,and a classifier for the development of classification *** DenseNet-169 is responsible for retrieving two separate sets of attributes,one from each of the left and right *** then,the SCNet will record the correlations between the two feature sets on a pixel-by-pixel *** the attributes have been analyzed,they are integrated to provide a representation at the patient *** the whole process of ODs categorization,the patient-level representation will be *** efficacy of the PLML_ODs is examined using a soft margin loss on a dataset that is readily accessible to the public,and the results reveal that the classification performance is significantly improved when compared to several baseline approaches.

关键词： Ocular disease multi-label spatial correlation CNN eye disease

来源：

维普期刊数据库评论

在线全文

维普期刊数据库

学校读者我要写书评

暂无评论

欢迎您,

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

在线全文

在线全文

请选择保存的检索档案：

请选择收藏分类：

通借通还

欢迎您,

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

在线全文

在线全文

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：