Can pre-trained convolutional neural networks be directly used as a feature extractor for video-based neonatal sleep and wake classification?

ABSTRACT:

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

SUBMITTER: Awais M 

PROVIDER: S-EPMC7641846 | biostudies-literature | 2020 Nov

REPOSITORIES: biostudies-literature