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Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images

Mohammad Khishe Orcid Logo, Fabio Caraffini Orcid Logo, Stefan Kuhn Orcid Logo

Mathematics, Volume: 9, Issue: 9, Start page: 1002

Swansea University Author: Fabio Caraffini Orcid Logo

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DOI (Published version): 10.3390/math9091002

Abstract

This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep...

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Published in: Mathematics
ISSN: 2227-7390
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa60908
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first_indexed 2022-09-23T10:12:44Z
last_indexed 2023-01-13T19:21:23Z
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spelling 2022-09-23T11:17:55.7409067 v2 60908 2022-08-28 Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2022-08-28 SCS This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19. Journal Article Mathematics 9 9 1002 MDPI AG 2227-7390 COVID-19; heuristic optimisation; deep convolutional neural networks; chest X-rays 28 4 2021 2021-04-28 10.3390/math9091002 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University This research received no external funding 2022-09-23T11:17:55.7409067 2022-08-28T19:17:06.9922623 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Mohammad Khishe 0000-0002-1024-8822 1 Fabio Caraffini 0000-0001-9199-7368 2 Stefan Kuhn 0000-0002-5990-4157 3 60908__25197__901c95edb41545008771c3682e67ce2c.pdf 60908_VoR.pdf 2022-09-23T11:13:11.8129654 Output 2890089 application/pdf Version of Record true © 2021 by the authors.This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
spellingShingle Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
Fabio Caraffini
title_short Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
title_full Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
title_fullStr Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
title_full_unstemmed Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
title_sort Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Mohammad Khishe
Fabio Caraffini
Stefan Kuhn
format Journal article
container_title Mathematics
container_volume 9
container_issue 9
container_start_page 1002
publishDate 2021
institution Swansea University
issn 2227-7390
doi_str_mv 10.3390/math9091002
publisher MDPI AG
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description This article proposes a framework that automatically designs classifiers for the early detection of COVID-19 from chest X-ray images. To do this, our approach repeatedly makes use of a heuristic for optimisation to efficiently find the best combination of the hyperparameters of a convolutional deep learning model. The framework starts with optimising a basic convolutional neural network which represents the starting point for the evolution process. Subsequently, at most two additional convolutional layers are added, at a time, to the previous convolutional structure as a result of a further optimisation phase. Each performed phase maximises the the accuracy of the system, thus requiring training and assessment of the new model, which gets gradually deeper, with relevant COVID-19 chest X-ray images. This iterative process ends when no improvement, in terms of accuracy, is recorded. Hence, the proposed method evolves the most performing network with the minimum number of convolutional layers. In this light, we simultaneously achieve high accuracy while minimising the presence of redundant layers to guarantee a fast but reliable model. Our results show that the proposed implementation of such a framework achieves accuracy up to 99.11%, thus being particularly suitable for the early detection of COVID-19.
published_date 2021-04-28T04:19:24Z
_version_ 1763754287806021632
score 11.037581

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