Deep Ensemble Model for COVID-19 Diagnosis and Classification Using Chest CT Images

• Published in: Biology (Basel, Switzerland) Vol. 11; no. 1; p. 43
• Main Authors: Ragab, Mahmoud, Eljaaly, Khalid, Alhakamy, Nabil A., Alhadrami, Hani A., Bahaddad, Adel A., Abo-Dahab, Sayed M., Khalil, Eied M.
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29.12.2021; MDPI
• Subjects: Algorithms; Artificial intelligence; Chest; Classification; Comparative analysis; Computed tomography; Coronaviruses; COVID-19; COVID-19 infection; Data collection; Datasets; Deep learning; Diagnosis; ensemble models; Learning algorithms; Long short-term memory; Machine learning; metaheuristics; Neural networks; Pandemics; Polymerase chain reaction; sharks; support vector machines; COVID-19; deep learning; metaheuristics; ensemble models; machine learning
• ISSN: 2079-7737; 2079-7737
• DOI: 10.3390/biology11010043

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.