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arXiv 2007.12496eess.IVcs.CV
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基于ILSVRC模型的增强结构帕金森病检测

Parkinson's Disease Detection with Ensemble Architectures based on ILSVRC Models

University of Alberta（阿尔伯塔大学）

Tahjid Ashfaque Mostafa, Irene Cheng

备注：
arXiv admin note: substantial text overlap with arXiv:2007.00682
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摘要：

在这项工作中，我们使用大脑的磁共振（MR）T1图像来探索各种神经网络架构，以识别帕金森病（PD），这是最常见的神经退行性疾病和运动障碍之一。我们提出了三种集成架构，结合了ImageNet大规模视觉识别挑战赛（ILSVRC）的一些获奖卷积神经网络模型。我们提出的所有架构优于现有的方法来检测PD从MR图像，达到高达95%的检测精度。我们还发现，当我们使用与PD无关的ImageNet数据集预训练的模型构建集成架构时，与没有任何预先训练的模型相比，检测性能明显更好。我们的发现表明，当没有或没有足够的训练数据时，这是一个有希望的方向。

英文摘要：

In this work, we explore various neural network architectures using Magnetic Resonance (MR) T1 images of the brain to identify Parkinson's Disease (PD), which is one of the most common neurodegenerative and movement disorders. We propose three ensemble architectures combining some winning Convolutional Neural Network models of ImageNet Large Scale Visual Recognition Challenge (ILSVRC). All of our proposed architectures outperform existing approaches to detect PD from MR images, achieving upto 95\% detection accuracy. We also find that when we construct our ensemble architecture using models pretrained on the ImageNet dataset unrelated to PD, the detection performance is significantly better compared to models without any prior training. Our finding suggests a promising direction when no or insufficient training data is available.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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