Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification
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. D...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/9360538/ |
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| author | El-Sayed M. El-Kenawy Seyedali Mirjalili Abdelhameed Ibrahim Mohammed Alrahmawy M. El-Said Rokaia M. Zaki Marwa Metwally Eid |
| author_facet | El-Sayed M. El-Kenawy Seyedali Mirjalili Abdelhameed Ibrahim Mohammed Alrahmawy M. El-Said Rokaia M. Zaki Marwa Metwally Eid |
| author_sort | El-Sayed M. El-Kenawy |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-18T01:33:56Z |
| format | Article |
| id | doaj.art-5aa1f64a55d540a8abf10c0db1993343 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-18T01:33:56Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5aa1f64a55d540a8abf10c0db19933432022-12-21T21:25:31ZengIEEEIEEE Access2169-35362021-01-019360193603710.1109/ACCESS.2021.30610589360538Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image ClassificationEl-Sayed M. El-Kenawy0https://orcid.org/0000-0002-9221-7658Seyedali Mirjalili1https://orcid.org/0000-0002-1443-9458Abdelhameed Ibrahim2https://orcid.org/0000-0002-8352-6731Mohammed Alrahmawy3https://orcid.org/0000-0001-8978-8268M. El-Said4Rokaia M. Zaki5https://orcid.org/0000-0002-7111-9389Marwa Metwally Eid6https://orcid.org/0000-0002-8557-3566Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura, EgyptCentre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD, AustraliaComputer Engineering and Control Systems Department, Faculty of Engineering, Mansoura University, Mansoura, EgyptDepartment of Computer Science, Faculty of Computers and Information, Mansoura University, Mansoura, EgyptElectrical Engineering Department, Faculty of Engineering, Mansoura University, Mansoura, EgyptDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura, EgyptDepartment of Communications and Electronics, Delta Higher Institute of Engineering and Technology (DHIET), Mansoura, EgyptThe 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.https://ieeexplore.ieee.org/document/9360538/Chest X-raytransfer learningconvolutional neural networksquirrel search optimizationmultilayer perceptronoptimization algorithm |
| spellingShingle | El-Sayed M. El-Kenawy Seyedali Mirjalili Abdelhameed Ibrahim Mohammed Alrahmawy M. El-Said Rokaia M. Zaki Marwa Metwally Eid Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification IEEE Access Chest X-ray transfer learning convolutional neural network squirrel search optimization multilayer perceptron optimization algorithm |
| title | Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification |
| title_full | Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification |
| title_fullStr | Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification |
| title_full_unstemmed | Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification |
| title_short | Advanced Meta-Heuristics, Convolutional Neural Networks, and Feature Selectors for Efficient COVID-19 X-Ray Chest Image Classification |
| title_sort | advanced meta heuristics convolutional neural networks and feature selectors for efficient covid 19 x ray chest image classification |
| topic | Chest X-ray transfer learning convolutional neural network squirrel search optimization multilayer perceptron optimization algorithm |
| url | https://ieeexplore.ieee.org/document/9360538/ |
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