Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50
Classification of Alzheimer's Disease with and without Imagery Using Gradient Boosted Machines and ResNet-50
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DOI：
10.3390/brainsci9090212
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发表时间：
2019-09-01
期刊：
影响因子：
3.3
通讯作者：
Fulton, Christopher P.
中科院分区：
文献类型：
--
作者：
Fulton, Lawrence, V;Dolezel, Diane;Fulton, Christopher P.
背景老年痴呆症是一种无法治愈的疾病。早期诊断阿尔茨海默病（AD）有助于计划生育和成本控制。本研究的目的是使用社会人口统计学、临床和磁共振成像（MRI）数据预测AD的存在。AD的早期检测可以实现计划生育，并可以通过延迟长期护理来降低成本。准确的非影像学方法也降低了患者成本。分析开放获取系列影像学研究（OASIS-1）横断面MRI数据。梯度增强机（GBM）预测AD的存在作为性别，年龄，教育，社会经济地位（SES）和简易精神状态检查（MMSE）的函数。具有50层的残差网络（ResNet-50）预测来自MRI（多类别分类）的临床痴呆评级（CDR）的存在和严重性。使用社会人口统计学和MMSE变量，GBM实现了平均91.3%的二分CDR预测准确性（10倍分层交叉验证）。MMSE是最重要的特征。ResNet-50使用基于80%训练集的图像生成技术，在Epoch 133的4139张图像（20%验证集）上实现了98.99%的三类预测准确率，并且在训练集上实现了近乎完美的多类预测准确率（99.34%）。机器学习方法对AD进行高准确度分类。GBM模型可以帮助提供基于非图像分析的初始检测，而ResNet-50网络模型可以帮助在提供者审查之前自动识别AD患者。
Background. Alzheimer's is a disease for which there is no cure. Diagnosing Alzheimer's disease (AD) early facilitates family planning and cost control. The purpose of this study is to predict the presence of AD using socio-demographic, clinical, and magnetic resonance imaging (MRI) data. Early detection of AD enables family planning and may reduce costs by delaying long-term care. Accurate, non-imagery methods also reduce patient costs. The Open Access Series of Imaging Studies (OASIS-1) cross-sectional MRI data were analyzed. A gradient boosted machine (GBM) predicted the presence of AD as a function of gender, age, education, socioeconomic status (SES), and a mini-mental state exam (MMSE). A residual network with 50 layers (ResNet-50) predicted the clinical dementia rating (CDR) presence and severity from MRI's (multi-class classification). The GBM achieved a mean 91.3% prediction accuracy (10-fold stratified cross validation) for dichotomous CDR using socio-demographic and MMSE variables. MMSE was the most important feature. ResNet-50 using image generation techniques based on an 80% training set resulted in 98.99% three class prediction accuracy on 4139 images (20% validation set) at Epoch 133 and nearly perfect multi-class predication accuracy on the training set (99.34%). Machine learning methods classify AD with high accuracy. GBM models may help provide initial detection based on non-imagery analysis, while ResNet-50 network models might help identify AD patients automatically prior to provider review.