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COVID-19 image classification using deep features and fractional-order marine predators algorithm
Faculty
Science
Year:
2020
Type of Publication:
ZU Hosted
Pages:
Authors:
Mohamed El Sayed Ahmed Muhamed
Staff Zu Site
Abstract In Staff Site
Journal:
Scientific Reports nature
Volume:
Keywords :
COVID-19 image classification using deep features
Abstract:
Currently, we witness the severe spread of the pandemic of the new Corona virus, COVID-19, which causes dangerous symptoms to humans and animals, its complications may lead to death. Although convolutional neural networks (CNNs) is considered the current state-of-the-art image classification technique, it needs massive computational cost for deployment and training. In this paper, we propose an improved hybrid classification approach for COVID-19 images by combining the strengths of CNNs (using a powerful architecture called Inception) to extract features and a swarm-based feature selection algorithm (Marine Predators Algorithm) to select the most relevant features. A combination of fractional-order and marine predators algorithm (FO-MPA) is considered an integration among a robust tool in mathematics named fractional-order calculus (FO). The proposed approach was evaluated on two public COVID-19 X-ray datasets which achieves both high performance and reduction of computational complexity. The two datasets consist of X-ray COVID-19 images by international Cardiothoracic radiologist, researchers and others published on Kaggle. The proposed approach selected successfully 130 and 86 out of 51 K features extracted by inception from dataset 1 and dataset 2, while improving classification accuracy at the same time. The results are the best achieved on these datasets when compared to a set of recent feature selection algorithms. By achieving 98.7%, 98.2% and 99.6%, 99% of classification accuracy and F-Score for dataset 1 and dataset 2, respectively, the proposed approach outperforms several CNNs and all recent works on COVID-19 images.
Author Related Publications
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Mohamed El Sayed Ahmed Muhamed, "A novel hybrid gradient-based optimizer and grey wolf optimizer feature selection method for human activity recognition using smartphone sensors", MDPI, 2021
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Mohamed El Sayed Ahmed Muhamed, "Efficient schemes for playout latency reduction in P2P-VOD systems", Springer, 2018
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Mohamed El Sayed Ahmed Muhamed, "a novel algorithm for source localization based on nonnegative matrix factroization using \alpha 'beta divergence in chochleagram", WSEAS, 2013
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Mohamed El Sayed Ahmed Muhamed, "Open cluster membership probability based on K-means clustering algorithm", Springer, 2016
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Department Related Publications
Heba Ibrahim Mustafa, "Soft Rough Approximation Operators on a Complete Atomic Boolean Lattice", Hindawi Publishing Corporation, 2013
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Heba Ibrahim Mustafa, "Generalized closed sets in ditopological texture spaces with application in rough set theory", Council for Innovative Research, 2013
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Haroun Mohammed Abdel-Fattah Barakat, "Statistical Modeling of Extreme Values with Applications to Air Pollution", Science Puplications publisher, 2012
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Fawzia Mahmoud Salim Mustafa, "soft generalized closed sets with respect to an ideal in soft topological spaces", http// dx.org/10.12785/amis/080225, 2014
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