A Transfer Learning Approach for Early Diagnosis of Alzheimer?s Disease on MRI Images
A Transfer Learning Approach for Early Diagnosis of Alzheimer?s Disease on MRI Images
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DOI：
10.1016/j.neuroscience.2021.01.002
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发表时间：
2021-03-05
期刊：
影响因子：
3.3
通讯作者：
Yaqub, Muhammad
中科院分区：
文献类型：
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作者：
Mehmood, Atif;Yang, Shuyuan;Yaqub, Muhammad
利用磁共振成像（MRI）检测轻度认知功能障碍（MCI），在早期治疗痴呆疾病中起着至关重要的作用。深度学习架构在此类研究中产生了令人印象深刻的结果。算法需要大量带注释的数据集来训练模型。在这项研究中，我们通过使用逐层迁移学习以及脑图像的组织分割来诊断阿尔茨海默病（AD）的早期阶段来克服这个问题。在逐层迁移学习中，我们使用了具有预训练权重的VGG架构家族。所提出的模型在正常对照（NC）、早期轻度认知障碍（EMCI）、晚期轻度认知障碍（LMCI）和AD之间分离。本文从阿尔茨海默病神经影像学倡议（ADNI）数据库中检索了85例NC患者、70例EMCI、70例LMCI和75例AD患者。对每个受试者进行组织分割以提取灰质（GM）组织。为了检验该方法的有效性，在预处理数据上对该方法进行了测试，AD与NC的分类准确率最高达到98.73%，EMCI与LMCI的分类准确率也达到83.72%，而其余类别的分类准确率均在80%以上。最后，我们提供了一个与其他研究的比较分析表明，该模型优于国家的最先进的模型在测试精度方面。(C)2021由Elsevier Ltd代表IBRO发布。
Mild cognitive impairment (MCI) detection using magnetic resonance image (MRI), plays a crucial role in the treatment of dementia disease at an early stage. Deep learning architecture produces impressive results in such research. Algorithms require a large number of annotated datasets for training the model. In this study, we overcome this issue by using layer-wise transfer learning as well as tissue segmentation of brain images to diagnose the early stage of Alzheimer's disease (AD). In layer-wise transfer learning, we used the VGG architecture family with pre-trained weights. The proposed model segregates between normal control (NC), the early mild cognitive impairment (EMCI), the late mild cognitive impairment (LMCI), and the AD. In this paper, 85 NC patients, 70 EMCI, 70 LMCI, and 75 AD patients access form the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. Tissue segmentation was applied on each subject to extract the gray matter (GM) tissue. In order to check the validity, the proposed method is tested on preprocessing data and achieved the highest rates of the classification accuracy on AD vs NC is 98.73%, also distinguish between EMCI vs LMCI patients testing accuracy 83.72%, whereas remaining classes accuracy is more than 80%. Finally, we provide a comparative analysis with other studies which shows that the proposed model outperformed the state-of-the-art models in terms of testing accuracy. (C) 2021 Published by Elsevier Ltd on behalf of IBRO.