利用GAN增强小型医疗数据集分类性能

IF 3.4 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Informatics Pub Date : 2023-03-08 DOI:10.3390/informatics10010028
Mohammad Alauthman, Ahmad Al-qerem, Bilal I. Sowan, A. Alsarhan, Mohammed Eshtay, A. Aldweesh, N. Aslam
{"title":"利用GAN增强小型医疗数据集分类性能","authors":"Mohammad Alauthman, Ahmad Al-qerem, Bilal I. Sowan, A. Alsarhan, Mohammed Eshtay, A. Aldweesh, N. Aslam","doi":"10.3390/informatics10010028","DOIUrl":null,"url":null,"abstract":"Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.","PeriodicalId":37100,"journal":{"name":"Informatics","volume":"10 1","pages":"28"},"PeriodicalIF":3.4000,"publicationDate":"2023-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Enhancing Small Medical Dataset Classification Performance Using GAN\",\"authors\":\"Mohammad Alauthman, Ahmad Al-qerem, Bilal I. Sowan, A. Alsarhan, Mohammed Eshtay, A. Aldweesh, N. Aslam\",\"doi\":\"10.3390/informatics10010028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.\",\"PeriodicalId\":37100,\"journal\":{\"name\":\"Informatics\",\"volume\":\"10 1\",\"pages\":\"28\"},\"PeriodicalIF\":3.4000,\"publicationDate\":\"2023-03-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Informatics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/informatics10010028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Informatics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/informatics10010028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 3

摘要

由于数据集有限,在医学领域开发有效的分类模型具有挑战性。为了解决这个问题,本研究提出使用生成对抗网络(GAN)作为数据增强技术。本研究旨在通过生成接近真实数据的合成数据来提高分类器的泛化性能、稳定性和精度。我们对13个基准医疗数据集采用特征选择和5种分类算法,并使用最小二乘GAN (LS-GAN)进行增强。使用不同比例的增强数据对生成的样本进行评估,结果表明支持向量机模型在更大样本下优于其他方法。提出的使用GAN的数据增强方法为提高医疗保健领域分类模型的性能提供了一个有前途的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Enhancing Small Medical Dataset Classification Performance Using GAN
Developing an effective classification model in the medical field is challenging due to limited datasets. To address this issue, this study proposes using a generative adversarial network (GAN) as a data-augmentation technique. The research aims to enhance the classifier’s generalization performance, stability, and precision through the generation of synthetic data that closely resemble real data. We employed feature selection and applied five classification algorithms to thirteen benchmark medical datasets, augmented using the least-square GAN (LS-GAN). Evaluation of the generated samples using different ratios of augmented data showed that the support vector machine model outperforms other methods with larger samples. The proposed data augmentation approach using a GAN presents a promising solution for enhancing the performance of classification models in the healthcare field.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Informatics
Informatics Social Sciences-Communication
CiteScore
6.60
自引率
6.50%
发文量
88
审稿时长
6 weeks
期刊最新文献
Simulation of discrete control systems with parallelism of behavior Formal description model and conditions for detecting linked coupling faults of the memory devices A model of homographs automatic identification for the Belarusian language Ontological analysis in the problems of container applications threat modelling Closed Gordon – Newell network with single-line poles and exponentially limited request waiting time
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1