Odusami Modupe, Maskeliūnas Rytis, Damaševičius Robertas, Krilavičius Tomas
Department of Multimedia Engineering, Kaunas University of Technology, 44249 Kaunas, Lithuania.
Department of Applied Informatics, Vytautas Magnus University, 44248 Kaunas, Lithuania.
Diagnostics (Basel). 2021 Jun 10;11(6):1071. doi: 10.3390/diagnostics11061071.
One of the first signs of Alzheimer's disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer's Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.
阿尔茨海默病（AD）的早期症状之一是轻度认知障碍（MCI），在中间阶段存在一些轻微的脑变化差异。尽管近年来对AD早期诊断的研究有所增加，但脑变化及其在功能磁共振成像（fMRI）中的复杂性使得AD的早期检测变得困难。本文提出了一种基于深度学习的方法，该方法可以预测MCI、早期MCI（EMCI）、晚期MCI（LMCI）和AD。使用由138名受试者组成的阿尔茨海默病神经影像倡议（ADNI）fMRI数据集进行评估。在EMCI与AD、LMCI与AD以及MCI与EMCI分类场景中，微调后的ResNet18网络分别达到了99.99%、99.95%和99.95%的分类准确率。在准确率、敏感性和特异性方面，所提出的模型比其他已知模型表现更好。
