COVID19 Classification Using CT Images via Ensembles of Deep Learning Models
- Authors
- Majid, Abdul; Khan, Muhammad Attique; Nam, Yunyoung; Tariq, Usman; Roy, Sudipta; Mostafa, Reham R.; Sakr, Rasha H.
- Issue Date
- 2021
- Publisher
- Tech Science Press
- Keywords
- COVID19; preprocessing; deep learning; information fusion; firefly algorithm; extreme learning machine
- Citation
- Computers, Materials and Continua, v.69, no.1, pp 319 - 337
- Pages
- 19
- Journal Title
- Computers, Materials and Continua
- Volume
- 69
- Number
- 1
- Start Page
- 319
- End Page
- 337
- URI
- https://scholarworks.bwise.kr/sch/handle/2021.sw.sch/19090
- DOI
- 10.32604/cmc.2021.016816
- ISSN
- 1546-2218
1546-2226
- Abstract
- The recent COVID-19 pandemic caused by the novel coronavirus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has had a significant impact on human life and the economy around the world. A reverse transcription polymerase chain reaction (RT-PCR) test is used to screen for this disease, but its low sensitivity means that it is not sufficient for early detection and treatment. As RT-PCR is a time-consuming procedure, there is interest in the introduction of automated techniques for diagnosis. Deep learning has a key role to play in the field of medical imaging. The most important issue in this area is the choice of key features. Here, we propose a set of deep learning features based on a system for automated classification of computed tomography (CT) images to identify COVID-19. Initially, this method was used to prepare a database of three classes: Pneumonia, COVID-19, and Healthy. The dataset consisted of 6000 CT images refined by a hybrid contrast stretching approach. In the next step, two advanced deep learning models (ResNet50 and DarkNet53) were fine-tuned and trained through transfer learning. The features were extracted from the second last feature layer of both models and further optimized using a hybrid optimization approach. For each deep model, the Rao-1 algorithm and the PSO algorithm were combined in the hybrid approach. Later, the selected features were merged using the new minimum parallel distance non-redundant (PMDNR) approach. The final fused vector was finally classified using the extreme machine classifier. The experimental process was carried out on a set of prepared data with an overall accuracy of 95.6%. Comparing the different classification algorithms at the different levels of the features demonstrated the reliability of the proposed framework.
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Collections - College of Engineering > Department of Computer Science and Engineering > 1. Journal Articles
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