PCA Based Hierarchical CNN for the Classification of Mild Cognitive Impairments and the Role of SIREN Activations

Harsh Bhasin, R. Agarwal, For Alzheimer's Disease
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引用次数: 0

Abstract

The use of Convolutional Neural Networks for the classification of volumetric data is contentious because 2-D convolutions miss out on the correlation between the slices of the volume, whilst 3-D networks guzzle extensive computing resources. Moreover, the advent of SIREN activations calls for the investigation regarding the role of activations in such networks. This work proposes a model that uses the Principal Component Analysis to reduce the given data, followed by a circumspectly designed CNN for extracting the pertinent features. The paper also investigates the role of activations in such networks. The method is used to classify the patients converted to Alzheimer's from Mild Cognitive Impairment from those who did not convert. The data is obtained from ADNI. The proposed work gives an accuracy of 94.29, which is better as compared to the state-of-the-art.
基于PCA的分层CNN轻度认知障碍分类及SIREN激活的作用
使用卷积神经网络对体积数据进行分类是有争议的,因为二维卷积忽略了体积切片之间的相关性,而三维网络消耗了大量的计算资源。此外,SIREN激活的出现要求对这些网络中激活的作用进行调查。这项工作提出了一个使用主成分分析来减少给定数据的模型,然后是一个精心设计的CNN来提取相关特征。本文还研究了激活在这些网络中的作用。该方法用于将轻度认知障碍转化为阿尔茨海默氏症的患者与未转化的患者进行分类。数据来自ADNI。所提出的工作给出了94.29的精度,这比最先进的技术要好。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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