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A hybrid CNN and ensemble model for COVID-19 lung infection detection on chest CT scans
Faculty
Computer Science
Year:
2023
Type of Publication:
ZU Hosted
Pages:
1-20
Authors:
Khalied Mohamed Hosny
Staff Zu Site
Abstract In Staff Site
Journal:
Plos one Plos
Volume:
18
Keywords :
, hybrid , , ensemble model , COVID-19 lung infection
Abstract:
COVID-19 is highly infectious and causes acute respiratory disease. Machine learning (ML) and deep learning (DL) models are vital in detecting disease from computerized chest tomography (CT) scans. The DL models outperformed the ML models. For COVID-19 detection from CT scan images, DL models are used as end-to-end models. Thus, the performance of the model is evaluated for the quality of the extracted feature and classification accuracy. There are four contributions included in this work. First, this research is motivated by studying the quality of the extracted feature from the DL by feeding these extracted to an ML model. In other words, we proposed comparing the end-to-end DL model performance against the approach of using DL for feature extraction and ML for the classification of COVID-19 CT scan images. Second, we proposed studying the effect of fusing extracted features from image descriptors, e.g., Scale-Invariant Feature Transform (SIFT), with extracted features from DL models. Third, we proposed a new Convolutional Neural Network (CNN) to be trained from scratch and then compared to the deep transfer learning on the same classification problem. Finally, we studied the performance gap between classic ML models against ensemble learning models. The proposed framework is evaluated using a CT dataset, where the obtained results are evaluated using five different metrics The obtained results revealed that using the proposed CNN model is better than using the well-known DL model for the purpose of feature extraction. Moreover, using a DL model for feature extraction and an ML model for the classification task achieved better results in comparison to using an end-to-end DL model for detecting COVID-19 CT scan images. Of note, the accuracy rate of the former method improved by using ensemble learning models instead of the classic ML models. The proposed method achieved the best accuracy rate of 99.39%.
Author Related Publications
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Khalied Mohamed Hosny, "New Graphical Ultimate Processor for Mapping Relational Database to Resource Description Framework", IEEE, 2022
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Khalied Mohamed Hosny, "Fast computation of accurate Zernike moments", Springer, 2008
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Khalied Mohamed Hosny, "Accurate Computation of QPCET for Color Images in Different Coordinate Systems", SPIE, 2017
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Department Related Publications
Ahmed Salah Mohamed Mostafa, "Lazy-Merge: A Novel Implementation for Indexed Parallel K-Way In-Place Merging", IEEE, 2016
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Ibrahiem Mahmoud Mohamed Elhenawy, "A Review on the Applications of Neutrosophic Sets", Source: Journal of Computational and Theoretical Nanoscience, Volume 13, Number 1, January 2016, pp. 936-944(9), 2016
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Mohammed Abdel Basset Metwally Attia, "A Review on the Applications of Neutrosophic Sets", Source: Journal of Computational and Theoretical Nanoscience, Volume 13, Number 1, January 2016, pp. 936-944(9), 2016
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Mohammed Abdel Basset Metwally Attia, "A Review on the Applications of Neutrosophic Sets", Source: Journal of Computational and Theoretical Nanoscience, Volume 13, Number 1, January 2016, pp. 936-944(9), 2016
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Mohammed Abdel Basset Metwally Attia, "A comparative study of cuckoo search and flower pollination algorithm on solving global optimization problems", emerald insight, 2017
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