Shearlet based Stacked Convolutional Network for Multiclass Diagnosis of Alzheimer’s Disease using the Florbetapir PET Amyloid Imaging Data

Emimal Jabason, M. Ahmad, M. Swamy
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引用次数: 7

Abstract

Although there is no cure for Alzheimer’s disease (AD), an accurate early diagnosis is essential for health and social care, and will be of great significance when the course of the disease could be reversed through treatment options. Florbetapir positron emission tomography (18F-AV-45 PET) is proven to be the most powerful imaging technique to investigate the deposition of amyloid plaques, one of the potential hallmarks of AD, signifying the onset of AD before it changes the brains structure. In this paper, we propose a novel classification algorithm to discriminate the patients having AD, early mild cognitive impairment (MCI), late MCI, and normal control in 18F-AV-45 PET using shearlet based deep convolutional neural network (CNN). It is known that the conventional CNNs involve convolution and pooling layers, which in fact produce the smoothed representation of data, and this results in losing detailed information. In view of this fact, the conventional CNN is integrated with shearlet transform incorporating the multiresolution details of the data. Once the model is pretrained to transform the input data into a better stacked representation, the resulting final layer is passed to softmax classifier, which returns the probabilities of each class. Through experimental results, it is shown that the performance of the proposed classification framework is superior to that of the traditional CNN in Alzheimer’s disease neuroimaging initiative (ADNI) database in terms of classification accuracy. As a result, it has the potential to distinguish the different stages of AD progression with less clinical prior information.
基于Shearlet的基于Florbetapir PET淀粉样蛋白成像数据的堆叠卷积网络多层次诊断阿尔茨海默病
虽然阿尔茨海默病(AD)无法治愈,但准确的早期诊断对于健康和社会保健至关重要,并且当可以通过治疗方案逆转疾病进程时将具有重要意义。Florbetapir正电子发射断层扫描(18F-AV-45 PET)被证明是研究淀粉样斑块沉积最有效的成像技术,淀粉样斑块是阿尔茨海默病的潜在标志之一,在阿尔茨海默病改变大脑结构之前就预示着它的发生。本文提出了一种基于shearlet的深度卷积神经网络(CNN)的18F-AV-45 PET分类算法,用于区分AD、早期轻度认知障碍(MCI)、晚期轻度认知障碍(MCI)和正常对照患者。众所周知,传统的cnn涉及卷积层和池化层,这实际上产生了数据的平滑表示,这导致了详细信息的丢失。鉴于此,将传统的CNN与shearlet变换相结合,融合了数据的多分辨率细节。一旦对模型进行预训练,将输入数据转换为更好的堆叠表示,生成的最后一层将传递给softmax分类器,该分类器返回每个类的概率。通过实验结果表明,本文提出的分类框架在分类准确率方面优于传统的CNN在Alzheimer’s disease neuroimaging initiative (ADNI)数据库。因此,它有可能在临床信息较少的情况下区分AD进展的不同阶段。
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