Department of Computer Science and Engineering, Northeastern University, Shenyang 110819, China.
Sensors (Basel). 2021 Nov 17;21(22):7634. doi: 10.3390/s21227634.
Alzheimer's disease (AD), the most common type of dementia, is a progressive disease beginning with mild memory loss, possibly leading to loss of the ability to carry on a conversation and respond to environments. It can seriously affect a person's ability to carry out daily activities. Therefore, early diagnosis of AD is conducive to better treatment and avoiding further deterioration of the disease. Magnetic resonance imaging (MRI) has become the main tool for humans to study brain tissues. It can clearly reflect the internal structure of a brain and plays an important role in the diagnosis of Alzheimer's disease. MRI data is widely used for disease diagnosis. In this paper, based on MRI data, a method combining a 3D convolutional neural network and ensemble learning is proposed to improve the diagnosis accuracy. Then, a data denoising module is proposed to reduce boundary noise. The experimental results on ADNI dataset demonstrate that the model proposed in this paper improves the training speed of the neural network and achieves 95.2% accuracy in AD vs. NC (normal control) task and 77.8% accuracy in sMCI (stable mild cognitive impairment) vs. pMCI (progressive mild cognitive impairment) task in the diagnosis of Alzheimer's disease.
阿尔茨海默病（AD）是最常见的痴呆症类型，是一种进行性疾病，始于轻度记忆丧失，可能导致无法进行对话和对环境做出反应。它会严重影响一个人的日常生活活动能力。因此，早期诊断 AD 有利于更好地治疗和避免疾病的进一步恶化。磁共振成像（MRI）已成为人类研究脑组织的主要工具。它可以清楚地反映大脑的内部结构，在阿尔茨海默病的诊断中起着重要作用。MRI 数据广泛用于疾病诊断。在本文中，基于 MRI 数据，提出了一种结合 3D 卷积神经网络和集成学习的方法来提高诊断准确性。然后，提出了一种数据去噪模块来减少边界噪声。在 ADNI 数据集上的实验结果表明，本文提出的模型提高了神经网络的训练速度，在 AD 与 NC（正常对照）任务中的准确率达到 95.2%，在 sMCI（稳定轻度认知障碍）与 pMCI（进行性轻度认知障碍）任务中的准确率达到 77.8%，实现了对阿尔茨海默病的诊断。
