Abstract

Background and objectives

Dementia is one of the brain diseases with serious symptoms such as memory loss, and thinking problems. According to the World Alzheimer Report 2016, in the world, there are 47 million people having dementia and it can be 131 million by 2050. There is no standard method to diagnose dementia, and consequently unable to access the treatment effectively. Hence, the computational diagnosis of the disease from brain Magnetic Resonance Image (MRI) scans plays an important role in supporting the early diagnosis. Alzheimer’s Disease (AD), a common type of Dementia, includes problems related to disorientation, mood swings, not managing self-care, and behavioral issues. In this article, we present a new computational method to diagnosis Alzheimer’s disease from 3D brain MR images.

Methods

An efficient approach to diagnosis Alzheimer’s disease from brain MRI scans is proposed comprising two phases: I) segmentation and II) classification, both based on deep learning. After the brain tissues are segmented by a model that combines Gaussian Mixture Model (GMM) and Convolutional Neural Network (CNN), a new model combining Extreme Gradient Boosting (XGBoost) and Support Vector Machine (SVM) is used to classify Alzheimer’s disease based on the segmented tissues.

Results

We present two evaluations for segmentation and classification. For comparison, the new method was evaluated using the AD-86 and AD-126 datasets leading to Dice 0.96 for segmentation in both datasets and accuracies 0.88, and 0.80 for classification, respectively.

Conclusion

Deep learning gives prominent results for segmentation and feature extraction in medical image processing. The combination of XGboost and SVM improves the results obtained.

中文翻译：

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使用深度学习对 3D 脑 MR 图像进行分割和分类的阿尔茨海默病诊断

摘要

背景和目标

痴呆症是一种具有严重症状的脑部疾病，例如记忆力减退和思维问题。根据《2016 年世界阿尔茨海默病报告》，全球有 4700 万人患有痴呆症，到 2050 年可能达到 1.31 亿人。痴呆症的诊断没有标准方法，因此无法有效获得治疗。因此，通过脑磁共振图像 (MRI) 扫描对疾病进行计算诊断在支持早期诊断方面发挥着重要作用。阿尔茨海默病 (AD) 是一种常见的痴呆症，包括与迷失方向、情绪波动、无法自我照顾和行为问题有关的问题。在本文中，我们提出了一种从 3D 脑 MR 图像诊断阿尔茨海默病的新计算方法。

方法

提出了一种通过脑部 MRI 扫描诊断阿尔茨海默病的有效方法，包括两个阶段：I) 分割和 II) 分类，均基于深度学习。将脑组织通过高斯混合模型（GMM）和卷积神经网络（CNN）相结合的模型进行分割后，采用极端梯度提升（XGBoost）和支持向量机（SVM）相结合的新模型对阿尔茨海默病进行分类。分割的组织。

结果

我们提出了两种用于分割和分类的评估。为了进行比较，使用 AD-86 和 AD-126 数据集对新方法进行了评估，导致数据集分割的 Dice 为 0.96，分类的准确度分别为 0.88 和 0.80。

结论

深度学习为医学图像处理中的分割和特征提取提供了显着的结果。XGboost 和 SVM 的结合改进了获得的结果。