Citation: Karmakar, M.; Choudhury,
B.; Patowary, R.; Nag, A. An
IoT-Based Deep Learning Framework
for Real-Time Detection of COVID-19
through Chest X-ray Images.
Computers 2023, 12, 8. https://
doi.org/10.3390/computers12010008
Academic Editors: Phivos Mylonas,
Katia Lida Kermanidis and
Manolis Maragoudakis
Received: 1 November 2022
Revised: 13 December 2022
Accepted: 19 December 2022
Published: 28 December 2022
Copyright: © 2022 by the authors.
Licensee MDPI, Basel, Switzerland.
This article is an open access article
distributed under the terms and
conditions of the Creative Commons
Attribution (CC BY) license (https://
creativecommons.org/licenses/by/
4.0/).
Article
An IoT-Based Deep Learning Framework for Real-Time
Detection of COVID-19 through Chest X-ray Images
Mithun Karmakar, Bikramjit Choudhury * , Ranjan Patowary and Amitava Nag
Department of CSE, Central Institute of Technology Kokrajhar, Assam 783370, India
* Correspondence: b.choudhury@cit.ac.in; Tel.: +91-8638005168
Abstract:
Over the next decade, Internet of Things (IoT) and the high-speed 5G network will be
crucial in enabling remote access to the healthcare system for easy and fast diagnosis. In this paper,
an IoT-based deep learning computer-aided diagnosis (CAD) framework is proposed for online
and real-time COVID-19 identification. The proposed work first fine-tuned the five state-of-the-art
deep CNN models such as Xception, ResNet50, DenseNet201, MobileNet, and VGG19 and then
combined these models into a majority voting deep ensemble CNN (DECNN) model in order to detect
COVID-19 accurately. The findings demonstrate that the suggested framework, with a test accuracy
of 98%, outperforms other relevant state-of-the-art methodologies in terms of overall performance.
The proposed CAD framework has the potential to serve as a decision support system for general
clinicians and rural health workers in order to diagnose COVID-19 at an early stage.
Keywords: Internet of Things; computer-aided diagnosis; COVID-19; deep ensemble CNN
1. Introduction
The coronavirus disease of 2019, also known as COVID-19, was first reported on 31 De-
cember 2019, in Wuhan city of China and quickly spread worldwide [
1
]. The COVID-19
pandemic has resulted in approximately 6,459,684 deaths and more than 596,873,121 con-
firmed cases of COVID-19 infection as of August 2022 [
2
]. Along with physical health,
COVID-19 also had a toll on mental health of people [
3
]. This has led to a major concern.
The pandemic’s management is complicated by the daily increase of positive COVID-19
cases and incorrect diagnoses. Due to the urgent need to diagnose COVID-19 disease, the
medical industry is on the lookout for new tools and strategies to track and manage the
virus’s spread [
4
]. The Internet of Medical Things (IoMT) and the high-speed 5G network,
in combination with artificial intelligence and deep learning techniques, could help the
healthcare system for quick diagnosis procedures in order to monitor COVID-19 disease
efficiently [
5
–
7
]. The IoMT has substantially shifted traditional healthcare systems to IoT-
based digitized healthcare infrastructure [
8
]. In order to release the loads of healthcare,
especially in a pandemic scenario, the IoMT provides real-time services in the healthcare
system [9].
The real-time reverse transcription-polymerase chain reaction (RT-PCR) test is the
most used technique for diagnosing COVID-19 [
10
]. However, this method of identification
requires a lot of time, and the outcomes might contain a lot of false-negative mistakes.
Many doctors regard chest X-rays to be one of the most basic diagnostic techniques [
11
]. It
is inexpensive and useful in identifying pulmonary infections such as pneumonia, tubercu-
losis, early lung cancer, and now COVID-19. Accurate diagnosis of COVID-19 using X-ray
images takes specialized expertise and experience. However, in the case of COVID-19 pan-
demic, the ratio of medical experts who can manually make this diagnosis to the number
of patients is inadequate. Computer-aided diagnosis (CAD) systems might be an effective
solution to address this deficiency during the COVID-19 pandemic [
12
]. In this paper, an
Internet of Medical Things (IoMT)-based deep learning CAD framework is presented for
Computers 2023, 12, 8. https://doi.org/10.3390/computers12010008 https://www.mdpi.com/journal/computers
