Learning Long-Term Temporal Features With Deep Neural Networks for Human Action Recognition

Yu, Shengquan; Xie, Lei; Liu, Lin; Xia, Daoxun

doi:10.1109/access.2019.2962284

Cited by 35 publications

(29 citation statements)

References 69 publications

(93 reference statements)

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“…CNNs, is a synthetic neural network, is one of machine learning algorithms. Several CNN models such as GoogLeNet [22] , VGG-Net [23] , ResNet [24] , and AlexNet [25] are popular at image classification in the last decade. Ardakani, Kanafi, Acharya, Khadem and Mohammadi [26] presented the state-of-the-art CNN architectures mentioned above to differentiate COVID-19 cases from other (non-COVID) cases.…”

Section: Introductionmentioning

confidence: 99%

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

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et al. 2021

Applied Soft Computing

254

171

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Section: Introductionmentioning

confidence: 99%

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Aslan

Ünlerşen

Sabancı

et al. 2021

Applied Soft Computing

254

171

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“…Convolutional neural networks (CNN), as a subset of machine learning algorithms, is a unique structure of synthetic neural networks used for image classification. There are several CNN models including AlexNet [14] , VGG-Net [15] , GoogLeNet [16] , and ResNet [17] . In the CNN models, classification accuracy correlates with the extended number of convolution layers [18] .…”

Section: Introductionmentioning

confidence: 99%

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

et al. 2020

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Diagnosis is a critical preventive step in Coronavirus research which has similar manifestations with other types of pneumonia. CT scans and X-rays play an important role in that direction. However, processing chest CT images and using them to accurately diagnose COVID-19 is a computationally expensive task. Machine Learning techniques have the potential to overcome this challenge. This paper proposes two optimization algorithms for feature selection and classification of COVID-19. The proposed framework has three cascaded phases. Firstly, the features are extracted from the CT scans using a Convolutional Neural Network (CNN) named AlexNet. Secondly, a proposed features selection algorithm, Guided Whale Optimization Algorithm (Guided WOA) based on Stochastic Fractal Search (SFS), is then applied followed by balancing the selected features. Finally, a proposed voting classifier, Guided WOA based on Particle Swarm Optimization (PSO), aggregates different classifiers' predictions to choose the most voted class. This increases the chance that individual classifiers, e.g. Support Vector Machine (SVM), Neural Networks (NN), k-Nearest Neighbor (KNN), and Decision Trees (DT), to show significant discrepancies. Two datasets are used to test the proposed model: CT images containing clinical findings of positive COVID-19 and CT images negative COVID-19. The proposed feature selection algorithm (SFS-Guided WOA) is compared with other optimization algorithms widely used in recent literature to validate its efficiency. The proposed voting classifier (PSO-Guided-WOA) achieved AUC (area under the curve) of 0.995 that is superior to other voting classifiers in terms of performance metrics. Wilcoxon rank-sum, ANOVA, and T-test statistical tests are applied to statistically assess the quality of the proposed algorithms as well.

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“…The classification accuracy of the four CNN models namely AlexNet [56] , VGGNet [57] , GoogLeNet [58] , and ResNet-50 [60] is claculated in this scenario for the tested chest X-ray dataset. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$TP$ \end{document} indicates true-positive value, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$FP$ \end{document} represents false-positive value, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$TN$ \end{document} indicates true-negative value, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$FN$ \end{document} represents false-negative value.…”

Section: Resultsmentioning

confidence: 99%

“…It can thus be used in applications such as clustering, detecting objects, and classifying images. Several CNN models have recently been introduced, such as AlexNet, [56] , VGGNet [57] , GoogLeNet [58] , Spotmole [59] and ResNet [60] . Convolution models used in the CNN models have different layers; higher classification accuracy is achieved if the number of convolution layers increases.…”

Section: Methodsmentioning

confidence: 99%

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Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

et al. 2021

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The chest X-ray is considered a significant clinical utility for basic examination and diagnosis. The human lung area can be affected by various infections, such as bacteria and viruses, leading to pneumonia. Efficient and reliable classification method facilities the diagnosis of such infections. Deep transfer learning has been introduced for pneumonia detection from chest X-rays in different models. However, there is still a need for further improvements in the feature extraction and advanced classification stages. This paper proposes a classification method with two stages to classify different cases from the chest X-ray images based on a proposed Advanced Squirrel Search Optimization Algorithm (ASSOA). The first stage is the feature learning and extraction processes based on a Convolutional Neural Network (CNN) model named ResNet-50 with image augmentation and dropout processes. The ASSOA algorithm is then applied to the extracted features for the feature selection process. Finally, the Multi-layer Perceptron (MLP) Neural Network’s connection weights are optimized by the proposed ASSOA algorithm (using the selected features) to classify input cases. A Kaggle chest X-ray images (Pneumonia) dataset consists of 5,863 X-rays is employed in the experiments. The proposed ASSOA algorithm is compared with the basic Squirrel Search (SS) optimization algorithm, Grey Wolf Optimizer (GWO), and Genetic Algorithm (GA) for feature selection to validate its efficiency. The proposed (ASSOA + MLP) is also compared with other classifiers, based on (SS + MLP), (GWO + MLP), and (GA + MLP), in performance metrics. The proposed (ASSOA + MLP) algorithm achieved a classification mean accuracy of (99.26%). The ASSOA + MLP algorithm also achieved a classification mean accuracy of (99.7%) for a chest X-ray COVID-19 dataset tested from GitHub. The results and statistical tests demonstrate the high effectiveness of the proposed method in determining the infected cases.

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Learning Long-Term Temporal Features With Deep Neural Networks for Human Action Recognition

Cited by 35 publications

References 69 publications

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

CNN-based transfer learning–BiLSTM network: A novel approach for COVID-19 infection detection

Novel Feature Selection and Voting Classifier Algorithms for COVID-19 Classification in CT Images

Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification

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