Vol.67, No.3, 2021, pp.3581-3599, doi:10.32604/cmc.2021.015399
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ARTICLE
CNN Ensemble Approach to Detect COVID-19 from Computed Tomography Chest Images
  • Haikel Alhichri*
Department Computer Engineering, Advanced Lab for Intelligent Systems, College of Computer and Information Sciences, King Saud University, Riyadh, 11543, Saudi Arabia
* Corresponding Author: Haikel Alhichri. Email:
(This article belongs to this Special Issue: Artificial Intelligence and Healthcare Analytics for COVID-19)
Received 19 November 2020; Accepted 16 January 2021; Issue published 01 March 2021
Abstract
In January 2020, the World Health Organization declared a global health emergency concerning the spread of a new coronavirus disease, which was later named COVID-19. Early and fast diagnosis and isolation of COVID-19 patients have proven to be instrumental in limiting the spread of the disease. Computed tomography (CT) is a promising imaging method for fast diagnosis of COVID-19. In this study, we develop a unique preprocessing step to resize CT chest images to a fixed size (256 × 256 pixels) that preserves the aspect ratio and reduces image loss. Then, we present a deep learning (DL) method to classify CT chest images based on the light-weight pre-trained EfficientNet-B3 CNN model and ensemble techniques. The proposed method, which we refer to as EfficientNet-B3-GAP-Ensemble, comprises an ensemble of a modified version of the EfficientNet-B3. We build the ensemble using multiple runs and multiple training epochs. We test the EfficientNet-B3-GAP-Ensemble on two common benchmark datasets, i.e., the COVID19-CT and SARS-CoV-2-CT datasets. The proposed method has outperformed state-of-the-art methods for both datasets. For the COVID19-CT dataset, it achieved 88.18% sensitivity, 88.29% precision, 88.18% accuracy, an F1-score of 88.15%, and AUC of 92.10%. With the SARS-CoV-2-CT dataset, we tested the proposed method under different train-test splits, i.e., 20%–80%, 50%–50%, and 80%–20%. For the latter split, the proposed method achieved 99.72% accuracy, 99.80% sensitivity, precision, and F1-scores, and an AUC score of 99.99%.
Keywords
COVID-19 detection; computed tomography; deep learning; convolutional neural network; EffecientNet-B3; ensemble techniques
Cite This Article
H. Alhichri and . , "Cnn ensemble approach to detect covid-19 from computed tomography chest images," Computers, Materials & Continua, vol. 67, no.3, pp. 3581–3599, 2021.
Citations
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