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 of seven neonates, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future work a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Can Pre-Trained Convolutional Neural Networks be directly used as a Feature Extractor for Video-based Neonatal Sleep and Wake Classification?

10.21203/rs.3.rs-56693/v3 ◽

2020 ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Fully Connected

Abstract Objective: In this paper, we propose to evaluate the use of a 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 feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very 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 neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Can Pre-Trained Convolutional Neural Networks be used as Feature Extractors for Video-based Neonatal Sleep and Wake Classification?

10.21203/rs.3.rs-56693/v1 ◽

2020 ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Fully Connected

Abstract Objective In this paper, we propose to evaluate the use of a pre-trained convolutional neural networks (CNN’s) 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 CNN’s as feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very expensive. The features are extracted after fully connected layers (FCL’s), where we compare several pre-trained CNN’s, e.g., VGG16, VGG19, InceptionV3, GoogLeNet, ResNet, and AlexNet. Results From around 2-h Fluke® video recording of seven neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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Can Pre-Trained Convolutional Neural Networks be used as Feature Extractors for Video-based Neonatal Sleep and Wake Classification?

10.21203/rs.3.rs-56693/v2 ◽

2020 ◽

Author(s):

Muhammad Awais ◽

Xi Long ◽

Bin Yin ◽

Chen chen ◽

Saeed Akbarzadeh ◽

...

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Video Recording ◽

Principal Component ◽

Classification Performance ◽

Support Vector ◽

Video Frames ◽

Feature Extractor ◽

Fully Connected

Abstract Objective: In this paper, we propose to evaluate the use of a 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 feature extractor would hugely reduce the effort of collecting new neonatal data for training a neural network which could be computationally very 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 neonate, we achieved a modest classification performance with an accuracy, sensitivity, and specificity of 65.3%, 69.8%, 61.0%, respectively with AlexNet using Fluke® (RGB) video frames. This indicates that using a pre-trained model as a feature extractor could not fully suffice for highly reliable sleep and wake classification in neonates. Therefore, in future a dedicated neural network trained on neonatal data or a transfer learning approach is required.

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A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8040160 ◽

2019 ◽

Vol 8 (4) ◽

pp. 160 ◽

Cited By ~ 11

Author(s):

Bingxin Liu ◽

Ying Li ◽

Guannan Li ◽

Anling Liu

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Machine Learning Algorithms ◽

Classification Model ◽

Support Vector ◽

Oil Film ◽

One Dimensional

Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

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Deep Convolutional Neural Networks for Hyperspectral Image Classification

Journal of Sensors ◽

10.1155/2015/258619 ◽

2015 ◽

Vol 2015 ◽

pp. 1-12 ◽

Cited By ~ 419

Author(s):

Wei Hu ◽

Yangyu Huang ◽

Li Wei ◽

Fan Zhang ◽

Hengchao Li

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Hyperspectral Image ◽

Image Data ◽

Classification Performance ◽

Spectral Signature ◽

Support Vector ◽

Data Sets ◽

Deep Convolutional Neural Networks ◽

Visual Tasks

Recently, convolutional neural networks have demonstrated excellent performance on various visual tasks, including the classification of common two-dimensional images. In this paper, deep convolutional neural networks are employed to classify hyperspectral images directly in spectral domain. More specifically, the architecture of the proposed classifier contains five layers with weights which are the input layer, the convolutional layer, the max pooling layer, the full connection layer, and the output layer. These five layers are implemented on each spectral signature to discriminate against others. Experimental results based on several hyperspectral image data sets demonstrate that the proposed method can achieve better classification performance than some traditional methods, such as support vector machines and the conventional deep learning-based methods.

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Remaining Useful Life Prediction from 3D Scan Data with Genetically Optimized Convolutional Neural Networks

10.20944/preprints202108.0272.v1 ◽

2021 ◽

Author(s):

Giovanni Diraco ◽

Pietro Siciliano ◽

Alessandro Leone

Keyword(s):

Neural Network ◽

Neural Networks ◽

Support Vector Regression ◽

Transfer Learning ◽

Convolutional Neural Networks ◽

Life Prediction ◽

Point Clouds ◽

Remaining Useful Life ◽

Support Vector ◽

Useful Life

In the current industrial landscape, increasingly pervaded by technological innovations, the adoption of optimized strategies for asset management is becoming a critical key success factor. Among the various strategies available, the “Prognostics and Health Management” strategy is able to support maintenance management decisions more accurately, through continuous monitoring of equipment health and “Remaining Useful Life” forecasting. In the present study, Convolutional Neural Network-based Deep Neural Network techniques are investigated for the Remaining Useful Life prediction of a punch tool, whose degradation is caused by working surface deformations during the machining process. Surface deformation is determined using a 3D scanning sensor capable of returning point clouds with micrometric accuracy during the operation of the punching machine, avoiding both downtime and human intervention. The 3D point clouds thus obtained are transformed into bidimensional image-type maps, i.e., maps of depths and normal vectors, to fully exploit the potential of convolutional neural networks for extracting features. Such maps are then processed by comparing 15 genetically optimized architectures with the transfer learning of 19 pre-trained models, using a classic machine learning approach, i.e., Support Vector Regression, as a benchmark. The achieved results clearly show that, in this specific case, optimized architectures provide performance far superior (MAPE=0.058) to that of transfer learning which, instead, remains at a lower or slightly higher level (MAPE=0.416) than Support Vector Regression (MAPE=0.857).

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Convolutional Neural Networks for Water Body Extraction from Landsat Imagery

International Journal of Computational Intelligence and Applications ◽

10.1142/s1469026817500018 ◽

2017 ◽

Vol 16 (01) ◽

pp. 1750001 ◽

Cited By ~ 23

Author(s):

Long Yu ◽

Zhiyin Wang ◽

Shengwei Tian ◽

Feiyue Ye ◽

Jianli Ding ◽

...

Keyword(s):

Neural Networks ◽

Deep Learning ◽

Convolutional Neural Networks ◽

Water Body ◽

Spatial Information ◽

Landsat Imagery ◽

Classification Performance ◽

Support Vector ◽

Learning Methods ◽

Wide Range

Traditional machine learning methods for water body extraction need complex spectral analysis and feature selection which rely on wealth of prior knowledge. They are time-consuming and hard to satisfy our request for accuracy, automation level and a wide range of application. We present a novel deep learning framework for water body extraction from Landsat imagery considering both its spectral and spatial information. The framework is a hybrid of convolutional neural networks (CNN) and logistic regression (LR) classifier. CNN, one of the deep learning methods, has acquired great achievements on various visual-related tasks. CNN can hierarchically extract deep features from raw images directly, and distill the spectral–spatial regularities of input data, thus improving the classification performance. Experimental results based on three Landsat imagery datasets show that our proposed model achieves better performance than support vector machine (SVM) and artificial neural network (ANN).

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Method for Training Convolutional Neural Networks for In Situ Plankton Image Recognition and Classification Based on the Mechanisms of the Human Eye

Sensors ◽

10.3390/s20092592 ◽

2020 ◽

Vol 20 (9) ◽

pp. 2592

Author(s):

Xuemin Cheng ◽

Yong Ren ◽

Kaichang Cheng ◽

Jie Cao ◽

Qun Hao

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Classification Accuracy ◽

Image Data ◽

Recall Rate ◽

Classification Performance ◽

Support Vector ◽

Coordinate Systems ◽

Polar Coordinate

In this study, we propose a method for training convolutional neural networks to make them identify and classify images with higher classification accuracy. By combining the Cartesian and polar coordinate systems when describing the images, the method of recognition and classification for plankton images is discussed. The optimized classification and recognition networks are constructed. They are available for in situ plankton images, exploiting the advantages of both coordinate systems in the network training process. Fusing the two types of vectors and using them as the input for conventional machine learning models for classification, support vector machines (SVMs) are selected as the classifiers to combine these two features of vectors, coming from different image coordinate descriptions. The accuracy of the proposed model was markedly higher than those of the initial classical convolutional neural networks when using the in situ plankton image data, with the increases in classification accuracy and recall rate being 5.3% and 5.1% respectively. In addition, the proposed training method can improve the classification performance considerably when used on the public CIFAR-10 dataset.

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A Lightweight Convolutional Neural Network Based on Channel Multi-Group Fusion for Remote Sensing Scene Classification

Remote Sensing ◽

10.3390/rs14010009 ◽

2021 ◽

Vol 14 (1) ◽

pp. 9

Author(s):

Cuiping Shi ◽

Xinlei Zhang ◽

Liguo Wang

Keyword(s):

Neural Network ◽

Remote Sensing ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Information Exchange ◽

Classification Performance ◽

Semantic Features ◽

Lack Of Information ◽

Group Fusion

With the development of remote sensing scene image classification, convolutional neural networks have become the most commonly used method in this field with their powerful feature extraction ability. In order to improve the classification performance of convolutional neural networks, many studies extract deeper features by increasing the depth and width of convolutional neural networks, which improves classification performance but also increases the complexity of the model. To solve this problem, a lightweight convolutional neural network based on channel multi-group fusion (LCNN-CMGF) is presented. For the proposed LCNN-CMGF method, a three-branch downsampling structure was designed to extract shallow features from remote sensing images. In the deep layer of the network, the channel multi-group fusion structure is used to extract the abstract semantic features of remote sensing scene images. The structure solves the problem of lack of information exchange between groups caused by group convolution through channel fusion of adjacent features. The four most commonly used remote sensing scene datasets, UCM21, RSSCN7, AID and NWPU45, were used to carry out a variety of experiments in this paper. The experimental results under the conditions of four datasets and multiple training ratios show that the proposed LCNN-CMGF method has more significant performance advantages than the compared advanced method.

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Identification of Earth’s surface objects using ensembles of convolutional neural networks.

Journal of the Belarusian State University. Mathematics and Informatics ◽

10.33581/2520-6508-2021-2-114-123 ◽

2021 ◽

pp. 114-123

Author(s):

Evgenii E. Marushko ◽

Alexander A. Doudkin ◽

Xiangtao Zheng

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Networks ◽

Support Vector ◽

Machine Learning Methods ◽

The Neural Network ◽

Vector Machines ◽

Identification Technique ◽

Optimal Values ◽

Fold Cross Validation

The paper proposes an identification technique of objects on the Earth’s surface images based on combination of machine learning methods. Different variants of multi-layer convolutional neural networks and support vector machines are considered as original models. A hybrid convolutional neural network that combines features extracted by the neural network and experts is proposed. Optimal values of hyperparameters of the models are calculated by grid search methods using k-fold cross-validation. The possibility of improving the accuracy of identification based on the ensembles of these models is shown. Effectiveness of the proposed technique is demonstrated by the example of images obtained by synthetic aperture radar.

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