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

• Main Authors: Mohammad Khishe, Fabio Caraffini, Stefan Kuhn
• Format: Article
• Language: English
• Published: MDPI AG 2021-04-01
• Series: Mathematics
• Subjects: COVID-19; heuristic optimisation; deep convolutional neural networks; chest X-rays
• Online Access: https://www.mdpi.com/2227-7390/9/9/1002

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 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.11</mn><mo>%</mo></mrow></semantics></math></inline-formula>, thus being particularly suitable for the early detection of COVID-19.