tetano
Editor, Senior Moderator
Biology (Basel)
. 2021 Dec 29;11(1):43.
doi: 10.3390/biology11010043.
Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images
Mahmoud Ragab[SUP] 1 2 [/SUP], Khalid Eljaaly[SUP] 3 [/SUP], Nabil A Alhakamy[SUP] 4 5 6 [/SUP], Hani A Alhadrami[SUP] 7 8 9 [/SUP], Adel A Bahaddad[SUP] 10 [/SUP], Sayed M Abo-Dahab[SUP] 11 [/SUP], Eied M Khalil[SUP] 12 13 [/SUP]
Affiliations
Abstract
Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.
Keywords: COVID-19; deep learning; ensemble models; machine learning; metaheuristics.
. 2021 Dec 29;11(1):43.
doi: 10.3390/biology11010043.
Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images
Mahmoud Ragab[SUP] 1 2 [/SUP], Khalid Eljaaly[SUP] 3 [/SUP], Nabil A Alhakamy[SUP] 4 5 6 [/SUP], Hani A Alhadrami[SUP] 7 8 9 [/SUP], Adel A Bahaddad[SUP] 10 [/SUP], Sayed M Abo-Dahab[SUP] 11 [/SUP], Eied M Khalil[SUP] 12 13 [/SUP]
Affiliations
- PMID: 35053041
- DOI: 10.3390/biology11010043
Abstract
Coronavirus disease 2019 (COVID-19) has spread worldwide, and medicinal resources have become inadequate in several regions. Computed tomography (CT) scans are capable of achieving precise and rapid COVID-19 diagnosis compared to the RT-PCR test. At the same time, artificial intelligence (AI) techniques, including machine learning (ML) and deep learning (DL), find it useful to design COVID-19 diagnoses using chest CT scans. In this aspect, this study concentrates on the design of an artificial intelligence-based ensemble model for the detection and classification (AIEM-DC) of COVID-19. The AIEM-DC technique aims to accurately detect and classify the COVID-19 using an ensemble of DL models. In addition, Gaussian filtering (GF)-based preprocessing technique is applied for the removal of noise and improve image quality. Moreover, a shark optimization algorithm (SOA) with an ensemble of DL models, namely recurrent neural networks (RNN), long short-term memory (LSTM), and gated recurrent unit (GRU), is employed for feature extraction. Furthermore, an improved bat algorithm with a multiclass support vector machine (IBA-MSVM) model is applied for the classification of CT scans. The design of the ensemble model with optimal parameter tuning of the MSVM model for COVID-19 classification shows the novelty of the work. The effectiveness of the AIEM-DC technique take place on benchmark CT image data set, and the results reported the promising classification performance of the AIEM-DC technique over the recent state-of-the-art approaches.
Keywords: COVID-19; deep learning; ensemble models; machine learning; metaheuristics.