Convolutional neural network based Alzheimer's disease classification from magnetic resonance brain images
Convolutional neural network based Alzheimer's disease classification from magnetic resonance brain images
复制标题
DOI：
10.1016/j.cogsys.2018.12.015
复制
发表时间：
2019-10-01
影响因子：
3.9
通讯作者：
Hemanth, D. Jude
中科院分区：
文献类型：
--
作者：
Jain, Rachna;Jain, Nikita;Hemanth, D. Jude
阿尔茨海默病是一种最常见的痴呆症，是一种神经退行性大脑疾病，目前尚无治愈方法。因此，利用计算机辅助系统进行此类疾病的早期诊断是研究人员非常重视和广泛研究的课题。如今，深度学习，特别是卷积神经网络（CNN）由于其在视觉对象分类、检测和分割等各种计算机视觉任务中的先进性能而受到越来越多的关注。最近几项使用大脑核磁共振扫描和深度学习的研究在诊断阿尔茨海默病方面显示出了令人鼓舞的结果。然而，像CNN这样的深度学习架构最常见的问题是，它们需要大量的数据进行训练。本文采用基于迁移学习的数学模型PFSECTL，其中使用ImageNet数据集训练的CNN架构VGG-16作为分类任务的特征提取器。实验数据收集自阿尔茨海默病神经影像学倡议（ADNI）数据库。对于验证集，使用所描述的方法进行三向分类的准确率为95.73%。(C) 2018 Elsevier B.V.版权所有
Alzheimer's disease, the most common form of dementia is a neurodegenerative brain order that has currently no cure for it. Hence, early diagnosis of such disease using computer-aided systems is a subject of great importance and extensive research amongst researchers. Nowadays, deep learning or particularly convolutional neural network (CNN) is getting more attention due to its state-of-the-art performances in variety of computer vision tasks such as visual object classification, detection and segmentation. Several recent studies, that have used brain MRI scans and deep learning have shown promising results for diagnosis of Alzheimer's disease. However, most common issue with deep learning architectures such as CNN is that they require large amount of data for training. In this paper, a mathematical model PFSECTL based on transfer learning is used in which a CNN architecture, VGG-16 trained on ImageNet dataset is used as a feature extractor for the classification task. Experimentation is performed on data collected from Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The accuracy of the 3-way classification using the described method is 95.73% for the validation set. (C) 2018 Elsevier B.V. All rights reserved.