论文标题
帕金森氏病使用MR图像的合奏结构检测
Parkinson's Disease Detection Using Ensemble Architecture from MR Images
论文作者
论文摘要
帕金森氏病（PD）是影响60岁以上患者的主要神经系统疾病之一。PD会导致认知障碍。在这项工作中，我们探索了使用磁共振（MR）T1大脑图像来识别帕金森氏症的各种方法。我们尝试结合了ImageNet大规模视觉识别挑战（ILSVRC）的一些获胜卷积神经网络模型的合奏体系结构，并提出了两个体系结构。我们发现，当我们专注于MR图像而不是使用整个MR图像的灰质（GM）和白质（WM）区域时，检测准确性会大大提高。我们使用平滑的GM和WM提取物以及我们提出的架构之一实现了94.7 \％的平均精度。我们还执行遮挡分析，并确定哪些大脑区域与体系结构决策过程相关。
Parkinson's Disease(PD) is one of the major nervous system disorders that affect people over 60. PD can cause cognitive impairments. In this work, we explore various approaches to identify Parkinson's using Magnetic Resonance (MR) T1 images of the brain. We experiment with ensemble architectures combining some winning Convolutional Neural Network models of ImageNet Large Scale Visual Recognition Challenge (ILSVRC) and propose two architectures. We find that detection accuracy increases drastically when we focus on the Gray Matter (GM) and White Matter (WM) regions from the MR images instead of using whole MR images. We achieved an average accuracy of 94.7\% using smoothed GM and WM extracts and one of our proposed architectures. We also perform occlusion analysis and determine which brain areas are relevant in the architecture decision making process.
