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Brain Age Estimation based on Brain MRI by an Ensemble of Deep Networks

机译:基于深网络集合的脑MRI基于脑MRI的脑年龄估计

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Estimation of biological brain age is one of the topics that has been much discussed in recent years. One of the most important reasons for this is the possibility of early detection of neurodegenerative disorders such as Alzheimer's and Parkinson's with Brain Age Estimation (BAE). Brain imaging is one of the most important data to estimate the biological age of the brain. Because the brain's natural aging follows a particular pattern, it enables researchers and physicians to predict the human brain's age from its degeneration. Some studies have been done on 2D or 3D brain images data for this purpose. In this study, an ensemble structure, including 3D and 2D Convolutional Neural Networks (CNNs), is used to BAE. The proposed ensemble CNN (ECNN) method obtained a Mean Absolute Error (MAE) of 3.57 years, which is better than the previous studies.
机译:生物脑年龄的估计是近年来讨论的话题之一。其中最重要的原因之一是早期检测神经退行性疾病,如阿尔茨海默和帕金森的脑年龄估计(BAE)。脑成像是估计大脑生物学年龄最重要的数据之一。因为大脑的自然老龄化遵循特定的模式,所以它使研究人员和医生能够从退化中预测人脑的年龄。为此目的,在2D或3D脑图像数据上完成了一些研究。在该研究中,使用包括3D和2D卷积神经网络(CNNS)的集合结构,用于BAE。所提出的集合CNN(ECNN)方法获得了3.57年的平均绝对误差(MAE),比以前的研究更好。

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