Citation: Mouhafid, M.; Salah, M.;
Yue, C.; Xia, K. Deep Ensemble
Learning-Based Models for Diagnosis
of COVID-19 from Chest CT Images.
Healthcare 2022, 10, 166. https://
doi.org/10.3390/healthcare10010166
Academic Editors: Keun Ho Ryu and
Nipon Theera-Umpon
Received: 3 December 2021
Accepted: 13 January 2022
Published: 15 January 2022
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Article
Deep Ensemble Learning-Based Models for Diagnosis of
COVID-19 from Chest CT Images
Mohamed Mouhafid * , Mokhtar Salah , Chi Yue and Kewen Xia
School of Electronics and Information Engineering, Hebei University of Technology, Tianjin 300401, China;
engalmokhtar@gmail.com (M.S.); chiyueliuxin@126.com (C.Y.); kwxia@hebut.edu.cn (K.X.)
* Correspondence: mohamed.mouhafid@outlook.com
Abstract:
Novel coronavirus (COVID-19) has been endangering human health and life since 2019.
The timely quarantine, diagnosis, and treatment of infected people are the most necessary and
important work. The most widely used method of detecting COVID-19 is real-time polymerase chain
reaction (RT-PCR). Along with RT-PCR, computed tomography (CT) has become a vital technique in
diagnosing and managing COVID-19 patients. COVID-19 reveals a number of radiological signatures
that can be easily recognized through chest CT. These signatures must be analyzed by radiologists. It
is, however, an error-prone and time-consuming process. Deep Learning-based methods can be used
to perform automatic chest CT analysis, which may shorten the analysis time. The aim of this study is
to design a robust and rapid medical recognition system to identify positive cases in chest CT images
using three Ensemble Learning-based models. There are several techniques in Deep Learning for
developing a detection system. In this paper, we employed Transfer Learning. With this technique,
we can apply the knowledge obtained from a pre-trained Convolutional Neural Network (CNN) to a
different but related task. In order to ensure the robustness of the proposed system for identifying
positive cases in chest CT images, we used two Ensemble Learning methods namely Stacking and
Weighted Average Ensemble (WAE) to combine the performances of three fine-tuned Base-Learners
(VGG19, ResNet50, and DenseNet201). For Stacking, we explored 2-Levels and 3-Levels Stacking. The
three generated Ensemble Learning-based models were trained on two chest CT datasets. A variety
of common evaluation measures (accuracy, recall, precision, and F1-score) are used to perform a
comparative analysis of each method. The experimental results show that the WAE method provides
the most reliable performance, achieving a high recall value which is a desirable outcome in medical
applications as it poses a greater risk if a true infected patient is not identified.
Keywords:
coronavirus detection; deep learning; convolutional neural network; transfer learning;
stacking; weighted average ensemble
1. Introduction
Since December 2019, COVID-19 has been featured in the media as a severe health
problem. This Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) is part of the
coronavirus family that gets transmitted through direct contact or by fomites. Symptoms
of coronavirus infection include fever, cough, fatigue, and a loss of taste. Coronavirus
can cause severe respiratory problems such as pneumonia, lung disorders, and kidney
malfunction in some cases. A serial interval of five to seven days and a reproduction rate of
two to three people make the virus very dangerous [
1
]. Several people are healthy carriers
of a virus, which causes between 5% and 10% of acute respiratory infections [
2
]. To stop
the spread of the COVID-19 infection, the timely quarantine, diagnosis, and treatment of
infected people are the most necessary and important work.
RT-PCR [
3
] and Enzyme-linked Immunosorbent Assay (ELISA) [
4
] are the most widely
used methods for identifying the novel coronavirus. RT-PCR is the primary screening pro-
cedure for identifying COVID-19 cases as it can detect the virus’ RNA in lower respiratory
Healthcare 2022, 10, 166. https://doi.org/10.3390/healthcare10010166 https://www.mdpi.com/journal/healthcare
