基于修复和深度集成模型的脑肿瘤检测

IF 1.1 Q3 INFORMATION SCIENCE & LIBRARY SCIENCE
Debendra Kumar Sahoo, Abhishek Das, M. Mohanty, Satyasis Mishra
{"title":"基于修复和深度集成模型的脑肿瘤检测","authors":"Debendra Kumar Sahoo, Abhishek Das, M. Mohanty, Satyasis Mishra","doi":"10.1080/02522667.2022.2091094","DOIUrl":null,"url":null,"abstract":"Abstract In this work, the authors have applied image inpainting on MRI images of the brain to highlight the tumors present in the image. These highlighted images are used for the training of the ensemble model. Three convolutional neural networks (CNNs) are used as the base classifier and their outputs are fed to a Multilayer Perceptron (MLP) for further training and final classification. Classification is done to check whether the brain is having a tumor or it is healthy using a data set that is available at Kaggle for open access. The proposed method provided 100% and 98.33% training and testing accuracies that show the effectiveness of applying inpainting on the data set images.","PeriodicalId":46518,"journal":{"name":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2022-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Brain tumor detection using inpainting and deep ensemble model\",\"authors\":\"Debendra Kumar Sahoo, Abhishek Das, M. Mohanty, Satyasis Mishra\",\"doi\":\"10.1080/02522667.2022.2091094\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Abstract In this work, the authors have applied image inpainting on MRI images of the brain to highlight the tumors present in the image. These highlighted images are used for the training of the ensemble model. Three convolutional neural networks (CNNs) are used as the base classifier and their outputs are fed to a Multilayer Perceptron (MLP) for further training and final classification. Classification is done to check whether the brain is having a tumor or it is healthy using a data set that is available at Kaggle for open access. The proposed method provided 100% and 98.33% training and testing accuracies that show the effectiveness of applying inpainting on the data set images.\",\"PeriodicalId\":46518,\"journal\":{\"name\":\"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2022-08-24\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/02522667.2022.2091094\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"INFORMATION SCIENCE & LIBRARY SCIENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/02522667.2022.2091094","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"INFORMATION SCIENCE & LIBRARY SCIENCE","Score":null,"Total":0}
引用次数: 1

摘要

摘要在这项工作中,作者对大脑的MRI图像进行了图像修复,以突出图像中存在的肿瘤。这些突出显示的图像用于集合模型的训练。三个卷积神经网络(CNNs)被用作基础分类器,它们的输出被馈送到多层感知器(MLP),用于进一步训练和最终分类。使用Kaggle提供的数据集进行分类,以检查大脑是否有肿瘤或是否健康。所提出的方法提供了100%和98.33%的训练和测试准确率,表明了在数据集图像上应用修复的有效性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Brain tumor detection using inpainting and deep ensemble model
Abstract In this work, the authors have applied image inpainting on MRI images of the brain to highlight the tumors present in the image. These highlighted images are used for the training of the ensemble model. Three convolutional neural networks (CNNs) are used as the base classifier and their outputs are fed to a Multilayer Perceptron (MLP) for further training and final classification. Classification is done to check whether the brain is having a tumor or it is healthy using a data set that is available at Kaggle for open access. The proposed method provided 100% and 98.33% training and testing accuracies that show the effectiveness of applying inpainting on the data set images.
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
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES
JOURNAL OF INFORMATION & OPTIMIZATION SCIENCES INFORMATION SCIENCE & LIBRARY SCIENCE-
自引率
21.40%
发文量
88
×
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