自动多模态融合技术用于阿尔茨海默病的人脑分类

B. Vivekanandam
{"title":"自动多模态融合技术用于阿尔茨海默病的人脑分类","authors":"B. Vivekanandam","doi":"10.36548/jeea.2021.3.005","DOIUrl":null,"url":null,"abstract":"Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.","PeriodicalId":10896,"journal":{"name":"Day 1 Tue, September 21, 2021","volume":"35 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2021-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder\",\"authors\":\"B. Vivekanandam\",\"doi\":\"10.36548/jeea.2021.3.005\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.\",\"PeriodicalId\":10896,\"journal\":{\"name\":\"Day 1 Tue, September 21, 2021\",\"volume\":\"35 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-11-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Day 1 Tue, September 21, 2021\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.36548/jeea.2021.3.005\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Day 1 Tue, September 21, 2021","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.36548/jeea.2021.3.005","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 13

摘要

阿尔茨海默氏症(AD)可能永久性损害记忆细胞,导致痴呆。研究人员表示,早期阿尔茨海默病的诊断是困难的。在临床试验中,MRI被用于检测AD。需要高鉴别性的MRI特征来准确划分痴呆的分期。由于大量的特征提取,改进的基于cnn的深度模型最近被证明是准确的。随着数据集中图像样本的减少,出现了过度拟合问题,限制了深度学习算法的有效性。本文通过融合技术使过拟合误差最小化。这种混合方法被用来比其他传统方法更准确地对阿尔茨海默病进行分类。此外,卷积神经网络(CNN)提供了比其他任何算法更多的MRI扫描图像微小变化的细微特征。因此,该算法在矢状、冠状、轴向轻度认知障碍(Mild Cognitive impairment, MCI)脑段分类中具有较高的准确性。此外,本文还将提出的算法与前人的研究成果进行了比较,以证明其优越性。性能指标使用健康受试者(HS)、MCI和迷你精神状态评估(MMSE)来评估提出的研究算法。
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
Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
Alzheimer's Disorder (AD) may permanently impair memory cells, resulting in dementia. Researchers say that early Alzheimer's disease diagnosis is difficult. MRI is used to detect AD in clinical trials. It requires high discriminative MRI characteristics to accurately classify dementia stages. Due to the large extraction of features, improved deep CNN-based models have recently proven accurate. With fewer picture samples in the datasets, over-fitting issues arise, limiting the effectiveness of deep learning algorithms. This research article minimizes the overfitting error due to fusion techniques. This hybrid approach is used to classify Alzheimer's disease more accurately than other traditional approaches. Besides, the Convolutional Neural Network (CNN) provides more minute features of small changes in MRI scan images than any other algorithm. Therefore, the proposed algorithm provides great accuracy in the region of sagittal, coronal, and axial Mild Cognitive Impairments (MCI) in the brain segment classification. Moreover, this research article compares the proposed algorithm with previous research output that is used to help prove its superiority. The performance metrics uses Health Subject (HS), MCI, and Mini-Mental State Evaluation (MMSE) to evaluate the proposed research algorithm.
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
自引率
0.00%
发文量
0
期刊最新文献
Pollination Inspired Clustering Model for Wireless Sensor Network Optimization Three Phase Coil based Optimized Wireless Charging System for Electric Vehicles Wireless Power Transfer Device Based on RF Energy Circuit and Transformer Coupling Procedure Hybrid Micro-Energy Harvesting Model using WSN for Self-Sustainable Wireless Mobile Charging Application Automated Multimodal Fusion Technique for the Classification of Human Brain on Alzheimer’s Disorder
×
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