Journal article 455 views 60 downloads
Evolving Deep Learning Convolutional Neural Networks for Early COVID-19 Detection in Chest X-ray Images
,
Fabio Caraffini ,
Stefan Kuhn
Mathematics, Volume: 9, Issue: 9, Start page: 1002
Swansea University Author:
Fabio Caraffini
-
PDF | Version of Record
© 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
Download (2.76MB)
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...
| Published in: | Mathematics |
|---|---|
| ISSN: | 2227-7390 |
| Published: |
MDPI AG
2021
|
| Online Access: |
Check full text
|
| URI: | https://cronfa.swan.ac.uk/Record/cronfa60908 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| 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 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. |
|---|---|
| Keywords: |
COVID-19; heuristic optimisation; deep convolutional neural networks; chest X-rays |
| College: |
Faculty of Science and Engineering |
| Funders: |
This research received no external funding |
| Issue: |
9 |
| Start Page: |
1002 |