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

Mohammad Khishe, Fabio Caraffini and Stefan Kuhn
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Mohammad Khishe: Department of Electronic Engineering, Imam Khomeini Marine Science University of Nowshahr, Nowshahr 16846-13114, Iran
Fabio Caraffini: Institute of Artificial Intelligence, De Montfort University, Leicester LE1 9BH, UK
Stefan Kuhn: Cyber Technology Institute, De Montfort University, Leicester LE1 9BH, UK

Mathematics, 2021, vol. 9, issue 9, 1-18

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 (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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