作者
Muhammad Awais, Xi Long, Bin Yin, Chen Chen, Saeed Akbarzadeh, Saadullah Farooq Abbasi, Muhammad Irfan, Chunmei Lu, Xinhua Wang, Laishuan Wang, Wei Chen
发表日期
2020/12
期刊
BMC research notes
卷号
13
页码范围
1-6
出版商
BioMed Central
简介
Objective
In this paper, we propose to evaluate the use of pre-trained convolutional neural networks (CNNs) as a features extractor followed by the Principal Component Analysis (PCA) to find the best discriminant features to perform classification using support vector machine (SVM) algorithm for neonatal sleep and wake states using Fluke® facial video frames. Using pre-trained CNNs as a feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNNs, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet.
Results
From around 2-h Fluke® video recording of seven neonates, we achieved a modest classification performance with an accuracy …
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M Awais, X Long, B Yin, C Chen, S Akbarzadeh… - BMC research notes, 2020