Classification of Shoulder Implant Manufacturer Using Pre-Trained DenseNet201 Combined With Capsule Network

IF 2.3 3区医学 Q2 SURGERY International Journal of Medical Robotics and Computer Assisted Surgery Pub Date : 2024-09-27 DOI:10.1002/rcs.2672

Xianzhong Jian, Zhenling Zhou, Wuwen Zhang

{"title":"Classification of Shoulder Implant Manufacturer Using Pre-Trained DenseNet201 Combined With Capsule Network","authors":"Xianzhong Jian, Zhenling Zhou, Wuwen Zhang","doi":"10.1002/rcs.2672","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Background</h3>\n \n <p>This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an <i>F</i>1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers.</p>\n </section>\n </div>","PeriodicalId":50311,"journal":{"name":"International Journal of Medical Robotics and Computer Assisted Surgery","volume":"20 5","pages":""},"PeriodicalIF":2.3000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Medical Robotics and Computer Assisted Surgery","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/rcs.2672","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"SURGERY","Score":null,"Total":0}

引用次数: 0

Abstract

Background

This study aims to accelerate revision surgery and treatment using X-ray imaging and deep learning to identify shoulder implant manufacturers in advance.

Methods

A feature engineering approach based on principal component analysis and a k-means algorithm was used to cluster shoulder implant data. In addition, a pre-trained DenseNet201 combined with a capsule network (DenseNet201-Caps) shoulder implant classification model was proposed.

Results

DenseNet201-Caps was the most effective classification model on the clustered dataset with an accuracy of 94.25% and an F1 score of 96.30%. Notably, clustering the dataset in advance improved the accuracy and the Caps implementations successfully enhanced the performance of all convolutional neural network models. The analysed results indicate that DenseNet201-Caps struggled to distinguish between the Cofield and Depuy manufacturers. Hence, a multistage classification approach was developed with an improved accuracy of 96.55% achieved.

Conclusions

The DenseNet201-Caps method enables the accurate identification of shoulder implant manufacturers.

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

使用预先训练的 DenseNet201 结合 Capsule 网络对肩关节植入物制造商进行分类。

背景：本研究旨在利用 X 射线成像和深度学习技术提前识别肩部植入物制造商，从而加速翻修手术和治疗：本研究旨在利用 X 射线成像和深度学习提前识别肩部植入物制造商，从而加速翻修手术和治疗：方法：采用基于主成分分析和 k-means 算法的特征工程方法对肩关节植入物数据进行聚类。此外，还提出了一个结合胶囊网络的预训练 DenseNet201（DenseNet201-Caps）肩关节植入物分类模型：结果：在聚类数据集上，DenseNet201-Caps 是最有效的分类模型，准确率为 94.25%，F1 得分为 96.30%。值得注意的是，提前对数据集进行聚类提高了准确率，而 Caps 实现成功提高了所有卷积神经网络模型的性能。分析结果表明，DenseNet201-Caps 难以区分 Cofield 和 Depuy 制造商。因此，我们开发了一种多级分类方法，准确率提高到 96.55%：结论：DenseNet201-Caps 方法能够准确识别肩部植入物制造商。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

International Journal of Medical Robotics and Computer Assisted Surgery 医学-外科

CiteScore

4.50

自引率

12.00%

发文量

131

审稿时长

6-12 weeks

期刊介绍： The International Journal of Medical Robotics and Computer Assisted Surgery provides a cross-disciplinary platform for presenting the latest developments in robotics and computer assisted technologies for medical applications. The journal publishes cutting-edge papers and expert reviews, complemented by commentaries, correspondence and conference highlights that stimulate discussion and exchange of ideas. Areas of interest include robotic surgery aids and systems, operative planning tools, medical imaging and visualisation, simulation and navigation, virtual reality, intuitive command and control systems, haptics and sensor technologies. In addition to research and surgical planning studies, the journal welcomes papers detailing clinical trials and applications of computer-assisted workflows and robotic systems in neurosurgery, urology, paediatric, orthopaedic, craniofacial, cardiovascular, thoraco-abdominal, musculoskeletal and visceral surgery. Articles providing critical analysis of clinical trials, assessment of the benefits and risks of the application of these technologies, commenting on ease of use, or addressing surgical education and training issues are also encouraged. The journal aims to foster a community that encompasses medical practitioners, researchers, and engineers and computer scientists developing robotic systems and computational tools in academic and commercial environments, with the intention of promoting and developing these exciting areas of medical technology.