基于改进分割模型的深度集成体系结构阿尔茨海默病检测。

Q3 Engineering
Shilpa Jaykumar Kale, Pramod U Chavan
{"title":"基于改进分割模型的深度集成体系结构阿尔茨海默病检测。","authors":"Shilpa Jaykumar Kale, Pramod U Chavan","doi":"10.1080/03091902.2025.2484691","DOIUrl":null,"url":null,"abstract":"<p><p>The most common cause of dementia, which includes significant cognitive impairment that interferes with day-to-day activities, is Alzheimer's Disease (AD). Deep learning techniques performed better on diagnostic tasks. However, current methods for detecting Alzheimer's disease lack effectiveness, resulting in inaccurate results. To overcome these challenges, a novel deep ensemble architecture for AD classification is proposed in this research. The proposed model involves key phases, including Preprocessing, Segmentation, Feature Extraction, and Classification. Initially, Median filtering is employed for preprocessing. Subsequently, an improved U-Net architecture is employed for segmentation, and then the features including Improved Shape Index Histogram (ISIH), Multi Binary Pattern (MBP), and Multi Texton are extracted from the segmented image. Then, an En-LeCILSTM is proposed, which combines the LeNet, CNN and improved LSTM models. Finally, the resultant output is obtained by averaging the intermediate output of each model, leading to improved detection accuracy. Finally, the proposed model's efficiency is assessed through various analyses, including classifier comparison, and performance metric evaluation. As a result, the En-LeCILSTM model scored a higher accuracy of 0.963 and an F-measure of 0.908, which surpasses the result of traditional methods. The outcomes demonstrate that the proposed model is notably more effective in detecting Alzheimer's disease.</p>","PeriodicalId":39637,"journal":{"name":"Journal of Medical Engineering and Technology","volume":" ","pages":"1-25"},"PeriodicalIF":0.0000,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deep ensemble architecture with improved segmentation model for Alzheimer's disease detection.\",\"authors\":\"Shilpa Jaykumar Kale, Pramod U Chavan\",\"doi\":\"10.1080/03091902.2025.2484691\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>The most common cause of dementia, which includes significant cognitive impairment that interferes with day-to-day activities, is Alzheimer's Disease (AD). Deep learning techniques performed better on diagnostic tasks. However, current methods for detecting Alzheimer's disease lack effectiveness, resulting in inaccurate results. To overcome these challenges, a novel deep ensemble architecture for AD classification is proposed in this research. The proposed model involves key phases, including Preprocessing, Segmentation, Feature Extraction, and Classification. Initially, Median filtering is employed for preprocessing. Subsequently, an improved U-Net architecture is employed for segmentation, and then the features including Improved Shape Index Histogram (ISIH), Multi Binary Pattern (MBP), and Multi Texton are extracted from the segmented image. Then, an En-LeCILSTM is proposed, which combines the LeNet, CNN and improved LSTM models. Finally, the resultant output is obtained by averaging the intermediate output of each model, leading to improved detection accuracy. Finally, the proposed model's efficiency is assessed through various analyses, including classifier comparison, and performance metric evaluation. As a result, the En-LeCILSTM model scored a higher accuracy of 0.963 and an F-measure of 0.908, which surpasses the result of traditional methods. The outcomes demonstrate that the proposed model is notably more effective in detecting Alzheimer's disease.</p>\",\"PeriodicalId\":39637,\"journal\":{\"name\":\"Journal of Medical Engineering and Technology\",\"volume\":\" \",\"pages\":\"1-25\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-04-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Medical Engineering and Technology\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1080/03091902.2025.2484691\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"Engineering\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Medical Engineering and Technology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/03091902.2025.2484691","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Engineering","Score":null,"Total":0}
引用次数: 0

摘要

阿尔茨海默病(AD)是痴呆症最常见的病因,它包括严重的认知障碍,干扰日常活动。深度学习技术在诊断任务上表现更好。然而,目前检测阿尔茨海默病的方法缺乏有效性,导致结果不准确。为了克服这些挑战,本研究提出了一种新的AD分类深度集成体系结构。该模型包括预处理、分割、特征提取和分类等关键阶段。首先采用中值滤波进行预处理。随后,采用改进的U-Net结构对分割图像进行分割,提取出改进形状指数直方图(ISIH)、多二进制模式(MBP)和多纹理(Multi Texton)特征。然后,结合LeNet、CNN和改进的LSTM模型,提出了一种En-LeCILSTM模型。最后,对每个模型的中间输出进行平均,得到结果输出,从而提高了检测精度。最后,通过分类器比较和性能指标评价等多种分析对模型的有效性进行了评价。结果表明,En-LeCILSTM模型的准确率为0.963,F-measure为0.908,优于传统方法的结果。结果表明,所提出的模型在检测阿尔茨海默病方面明显更有效。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Deep ensemble architecture with improved segmentation model for Alzheimer's disease detection.

The most common cause of dementia, which includes significant cognitive impairment that interferes with day-to-day activities, is Alzheimer's Disease (AD). Deep learning techniques performed better on diagnostic tasks. However, current methods for detecting Alzheimer's disease lack effectiveness, resulting in inaccurate results. To overcome these challenges, a novel deep ensemble architecture for AD classification is proposed in this research. The proposed model involves key phases, including Preprocessing, Segmentation, Feature Extraction, and Classification. Initially, Median filtering is employed for preprocessing. Subsequently, an improved U-Net architecture is employed for segmentation, and then the features including Improved Shape Index Histogram (ISIH), Multi Binary Pattern (MBP), and Multi Texton are extracted from the segmented image. Then, an En-LeCILSTM is proposed, which combines the LeNet, CNN and improved LSTM models. Finally, the resultant output is obtained by averaging the intermediate output of each model, leading to improved detection accuracy. Finally, the proposed model's efficiency is assessed through various analyses, including classifier comparison, and performance metric evaluation. As a result, the En-LeCILSTM model scored a higher accuracy of 0.963 and an F-measure of 0.908, which surpasses the result of traditional methods. The outcomes demonstrate that the proposed model is notably more effective in detecting Alzheimer's disease.

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 Medical Engineering and Technology
Journal of Medical Engineering and Technology Engineering-Biomedical Engineering
CiteScore
4.60
自引率
0.00%
发文量
77
期刊介绍: The Journal of Medical Engineering & Technology is an international, independent, multidisciplinary, bimonthly journal promoting an understanding of the physiological processes underlying disease processes and the appropriate application of technology. Features include authoritative review papers, the reporting of original research, and evaluation reports on new and existing techniques and devices. Each issue of the journal contains a comprehensive information service which provides news relevant to the world of medical technology, details of new products, book reviews, and selected contents of related journals.
×
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