基于深度学习的MRI图像早期诊断阿尔茨海默病集成方法。

IF 2.7 4区 医学 Q2 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS Neuroinformatics Pub Date : 2024-01-01 Epub Date: 2023-12-02 DOI:10.1007/s12021-023-09646-2
Sina Fathi, Ali Ahmadi, Afsaneh Dehnad, Mostafa Almasi-Dooghaee, Melika Sadegh
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引用次数: 0

摘要

最近,由于阿尔茨海默病的发病率越来越高,以及由此给个人和社会带来的成本,早期诊断得到了广泛关注。本研究的主要目的是提出一种基于深度学习的集成方法,用于MRI图像对AD的早期诊断。本研究的方法包括收集数据集,预处理,创建单个和集成模型,基于ADNI数据评估模型,以及基于局部数据集验证训练模型。所提出的方法是通过对各种集成场景的比较分析选择的集成方法。最后,选取6个最佳的基于cnn的分类器进行组合,构成集成模型。NC/AD、NC/EMCI、EMCI/LMCI、LMCI/AD、四向分类和三向分类的准确率分别为98.57、96.37、94.22、99.83、93.88和93.92。在本地数据集上的验证结果显示,三向分类的准确率为88.46。我们的表现结果高于大多数被审查的研究,并与其他研究相当。虽然对比分析显示了集成方法对单个体系结构的优越结果,但各种集成方法之间没有显着差异。验证结果表明,个别模型在实际应用中表现不佳。相比之下,集合方法显示出令人满意的结果。然而,需要对各种更大的数据集进行进一步的研究来验证模型的泛化性。
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A Deep Learning-Based Ensemble Method for Early Diagnosis of Alzheimer's Disease using MRI Images.

Recently, the early diagnosis of Alzheimer's disease has gained major attention due to the growing prevalence of the disease and the resulting costs imposed on individuals and society. The main objective of this study was to propose an ensemble method based on deep learning for the early diagnosis of AD using MRI images. The methodology of this study consisted of collecting the dataset, preprocessing, creating the individual and ensemble models, evaluating the models based on ADNI data, and validating the trained model based on the local dataset. The proposed method was an ensemble approach selected through a comparative analysis of various ensemble scenarios. Finally, the six best individual CNN-based classifiers were selected to combine and constitute the ensemble model. The evaluation showed an accuracy rate of 98.57, 96.37, 94.22, 99.83, 93.88, and 93.92 for NC/AD, NC/EMCI, EMCI/LMCI, LMCI/AD, four-way and three-way classification groups, respectively. The validation results on the local dataset revealed an accuracy of 88.46 for three-way classification. Our performance results were higher than most reviewed studies and comparable with others. Although comparative analysis showed superior results of ensemble methods against individual architectures, there were no significant differences among various ensemble approaches. The validation results revealed the low performance of individual models in practice. In contrast, the ensemble method showed promising results. However, further studies on various and larger datasets are required to validate the generalizability of the model.

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来源期刊
Neuroinformatics
Neuroinformatics 医学-计算机:跨学科应用
CiteScore
6.00
自引率
6.70%
发文量
54
审稿时长
3 months
期刊介绍: Neuroinformatics publishes original articles and reviews with an emphasis on data structure and software tools related to analysis, modeling, integration, and sharing in all areas of neuroscience research. The editors particularly invite contributions on: (1) Theory and methodology, including discussions on ontologies, modeling approaches, database design, and meta-analyses; (2) Descriptions of developed databases and software tools, and of the methods for their distribution; (3) Relevant experimental results, such as reports accompanie by the release of massive data sets; (4) Computational simulations of models integrating and organizing complex data; and (5) Neuroengineering approaches, including hardware, robotics, and information theory studies.
期刊最新文献
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