Prediction of COVID-19 from Chest CT Images Using an Ensemble of Deep Learning Models
Open Access
- 29 July 2021
- journal article
- research article
- Published by MDPI AG in Applied Sciences
- Vol. 11 (15), 7004
- https://doi.org/10.3390/app11157004
Abstract
The novel SARS-CoV-2 virus, responsible for the dangerous pneumonia-type disease, COVID-19, has undoubtedly changed the world by killing at least 3,900,000 people as of June 2021 and compromising the health of millions across the globe. Though the vaccination process has started, in developing countries such as India, the process has not been fully developed. Thereby, a diagnosis of COVID-19 can restrict its spreading and level the pestilence curve. As the quickest indicative choice, a computerized identification framework ought to be carried out to hinder COVID-19 from spreading more. Meanwhile, Computed Tomography (CT) imaging reveals that the attributes of these images for COVID-19 infected patients vary from healthy patients with or without other respiratory diseases, such as pneumonia. This study aims to establish an effective COVID-19 prediction model through chest CT images using efficient transfer learning (TL) models. Initially, we used three standard deep learning (DL) models, namely, VGG-16, ResNet50, and Xception, for the prediction of COVID-19. After that, we proposed a mechanism to combine the above-mentioned pre-trained models for the overall improvement of the prediction capability of the system. The proposed model provides 98.79% classification accuracy and a high -score of 0.99 on the publicly available SARS-CoV-2 CT dataset. The model proposed in this study is effective for the accurate screening of COVID-19 CT scans and, hence, can be a promising supplementary diagnostic tool for the forefront clinical specialists.
This publication has 50 references indexed in Scilit:
- Building predictive models for MERS-CoV infections using data mining techniquesJournal of Infection and Public Health, 2016
- Ensemble Methods for Continuous Affect RecognitionPublished by Association for Computing Machinery (ACM) ,2015
- Deep learningNature, 2015
- Neural Network Ensembles in Reinforcement LearningNeural Processing Letters, 2013
- Student’s t-TestsPublished by Springer Science and Business Media LLC ,2011
- A Survey on Transfer LearningIEEE Transactions on Knowledge and Data Engineering, 2009
- Distinctive Image Features from Scale-Invariant KeypointsInternational Journal of Computer Vision, 2004
- Real-Time Reverse Transcription–Polymerase Chain Reaction Assay for SARS-associated CoronavirusEmerging Infectious Diseases, 2004
- Support vector machinesIEEE Intelligent Systems and their Applications, 1998
- Stacked generalizationNeural Networks, 1992