Breast cancer detection employing stacked ensemble model with convolutional features.

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Hanen Karamti, Raed Alharthi, Muhammad Umer, Hadil Shaiba, Abid Ishaq, Nihal Abuzinadah, Shtwai Alsubai, Imran Ashraf
{"title":"Breast cancer detection employing stacked ensemble model with convolutional features.","authors":"Hanen Karamti, Raed Alharthi, Muhammad Umer, Hadil Shaiba, Abid Ishaq, Nihal Abuzinadah, Shtwai Alsubai, Imran Ashraf","doi":"10.3233/CBM-230294","DOIUrl":null,"url":null,"abstract":"<p><p>Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.</p>","PeriodicalId":2,"journal":{"name":"ACS Applied Bio Materials","volume":null,"pages":null},"PeriodicalIF":4.6000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11322706/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"ACS Applied Bio Materials","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.3233/CBM-230294","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, BIOMATERIALS","Score":null,"Total":0}
引用次数: 0

Abstract

Breast cancer is a major cause of female deaths, especially in underdeveloped countries. It can be treated if diagnosed early and chances of survival are high if treated appropriately and timely. For timely and accurate automated diagnosis, machine learning approaches tend to show better results than traditional methods, however, accuracy lacks the desired level. This study proposes the use of an ensemble model to provide accurate detection of breast cancer. The proposed model uses the random forest and support vector classifier along with automatic feature extraction using an optimized convolutional neural network (CNN). Extensive experiments are performed using the original, as well as, CNN-based features to analyze the performance of the deployed models. Experimental results involving the use of the Wisconsin dataset reveal that CNN-based features provide better results than the original features. It is observed that the proposed model achieves an accuracy of 99.99% for breast cancer detection. Performance comparison with existing state-of-the-art models is also carried out showing the superior performance of the proposed model.

利用具有卷积特征的堆叠集合模型检测乳腺癌。
乳腺癌是女性死亡的主要原因,尤其是在不发达国家。如果早期诊断,乳腺癌是可以治疗的,而且如果治疗得当、及时,存活的几率也很高。为了及时、准确地进行自动诊断,机器学习方法往往比传统方法显示出更好的效果,但准确性还达不到预期水平。本研究建议使用集合模型来准确检测乳腺癌。建议的模型使用随机森林和支持向量分类器,并使用优化的卷积神经网络(CNN)进行自动特征提取。我们使用原始特征和基于 CNN 的特征进行了大量实验,以分析所部署模型的性能。使用威斯康星数据集的实验结果表明,基于 CNN 的特征比原始特征提供了更好的结果。据观察,所提出的模型在乳腺癌检测方面达到了 99.99% 的准确率。与现有的最先进模型进行的性能比较也显示了所提出模型的优越性能。
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 求助全文
来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信