Mehmood Atif, Maqsood Muazzam, Bashir Muzaffar, Shuyuan Yang
School of Artificial Intelligence, Xidian University, No. 2 South Taibai Road, Xian 710071, China.
Department of Computer Science, COMSATS University Islamabad, Attock Campus, Attock 43600, Pakistan.
Brain Sci. 2020 Feb 5;10(2):84. doi: 10.3390/brainsci10020084.
Alzheimer's disease (AD) may cause damage to the memory cells permanently, which results in the form of dementia. The diagnosis of Alzheimer's disease at an early stage is a problematic task for researchers. For this, machine learning and deep convolutional neural network (CNN) based approaches are readily available to solve various problems related to brain image data analysis. In clinical research, magnetic resonance imaging (MRI) is used to diagnose AD. For accurate classification of dementia stages, we need highly discriminative features obtained from MRI images. Recently advanced deep CNN-based models successfully proved their accuracy. However, due to a smaller number of image samples available in the datasets, there exist problems of over-fitting hindering the performance of deep learning approaches. In this research, we developed a Siamese convolutional neural network (SCNN) model inspired by VGG-16 (also called Oxford Net) to classify dementia stages. In our approach, we extend the insufficient and imbalanced data by using augmentation approaches. Experiments are performed on a publicly available dataset open access series of imaging studies (OASIS), by using the proposed approach, an excellent test accuracy of 99.05% is achieved for the classification of dementia stages. We compared our model with the state-of-the-art models and discovered that the proposed model outperformed the state-of-the-art models in terms of performance, efficiency, and accuracy.
阿尔茨海默病（AD）可能会对记忆细胞造成永久性损伤，从而导致痴呆症。对研究人员来说，早期诊断阿尔茨海默病是一项颇具挑战性的任务。为此，基于机器学习和深度卷积神经网络（CNN）的方法可用于解决与脑图像数据分析相关的各种问题。在临床研究中，磁共振成像（MRI）用于诊断AD。为了准确分类痴呆阶段，我们需要从MRI图像中获得高度有区分性的特征。最近，先进的基于深度CNN的模型成功证明了其准确性。然而，由于数据集中可用的图像样本数量较少，存在过拟合问题，阻碍了深度学习方法的性能。在本研究中，我们受VGG - 16（也称为牛津网络）启发，开发了一种暹罗卷积神经网络（SCNN）模型来对痴呆阶段进行分类。在我们的方法中，我们通过使用增强方法来扩充不足和不平衡的数据。在一个公开可用的数据集——开放获取影像研究系列（OASIS）上进行了实验，通过所提出的方法，在痴呆阶段分类方面实现了99.05%的出色测试准确率。我们将我们的模型与最先进的模型进行了比较，发现所提出的模型在性能、效率和准确性方面优于最先进的模型。
