DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images
DEMNET: A Deep Learning Model for Early Diagnosis of Alzheimer Diseases and Dementia From MR Images
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DOI：
10.1109/access.2021.3090474
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发表时间：
2021-01-01
期刊：
影响因子：
3.9
通讯作者：
Manoharan, S.
中科院分区：
文献类型：
--
作者：
Murugan, Suriya;Venkatesan, Chandran;Manoharan, S.
阿尔茨海默病（AD）是全球痴呆症最常见的原因。它从轻微到严重，逐渐削弱了一个人在没有帮助的情况下完成任何工作的能力。由于人口年龄和诊断时间轴，它开始超过。对于分类病例，现有的方法包括病史，神经心理学测试和磁共振成像（MRI），但由于缺乏灵敏度和精确度，有效的程序仍然不一致。卷积神经网络（CNN）用于创建一个框架，可用于从MRI图像中检测特定的阿尔茨海默病特征。通过考虑痴呆症的四个阶段并进行特定的诊断，该模型生成了从局部大脑结构到多层感知器的高分辨率疾病概率图，并提供了个体阿尔茨海默病风险的准确，直观的可视化。为了避免类别不平衡的问题，样本应该在类别之间均匀分布。从Kaggle获得的MRI图像数据集具有主要的类不平衡问题。提出了一种痴呆网络（DEMNET），用于从MRI中检测痴呆阶段。DEMNET从Kaggle数据集获得了95.23%的准确度，97%的曲线下面积（AUC）和0.93的Cohen Kappa值，这上级现有的方法。我们还使用阿尔茨海默病神经影像学倡议（ADNI）数据集来预测AD类别，以评估所提出的模型的有效性。
Alzheimer's Disease (AD) is the most common cause of dementia globally. It steadily worsens from mild to severe, impairing one's ability to complete any work without assistance. It begins to outstrip due to the population ages and diagnosis timeline. For classifying cases, existing approaches incorporate medical history, neuropsychological testing, and Magnetic Resonance Imaging (MRI), but efficient procedures remain inconsistent due to lack of sensitivity and precision. The Convolutional Neural Network (CNN) is utilized to create a framework that can be used to detect specific Alzheimer's disease characteristics from MRI images. By considering four stages of dementia and conducting a particular diagnosis, the proposed model generates high-resolution disease probability maps from the local brain structure to a multilayer perceptron and provides accurate, intuitive visualizations of individual Alzheimer's disease risk. To avoid the problem of class imbalance, the samples should be evenly distributed among the classes. The obtained MRI image dataset from Kaggle has a major class imbalance problem. A DEMentia NETwork (DEMNET) is proposed to detect the dementia stages from MRI. The DEMNET achieves an accuracy of 95.23%, Area Under Curve (AUC) of 97% and Cohen's Kappa value of 0.93 from the Kaggle dataset, which is superior to existing methods. We also used the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset to predict AD classes in order to assess the efficacy of the proposed model.