A genetic programming-based convolutional deep learning algorithm for identifying COVID-19 cases via X-ray images

Evolutionary algorithms have been successfully employed to find the best structure for many learning algorithms including neural networks. Due to their flexibility and promising results, Convolutional Neural Networks (CNNs) have found their application in many image processing applications. The structure of CNNs greatly affects the performance of these algorithms both in terms of accuracy and computational cost, thus, finding the best architecture for these networks is a crucial task before they are employed. In this paper, we develop a genetic programming approach for the optimization of CNN structure in diagnosing COVID-19 cases via X-ray images. A graph representation for CNN architecture is proposed and evolutionary operators including crossover and mutation are specifically designed for the proposed representation. The proposed architecture of CNNs is defined by two sets of parameters, one is the skeleton which determines the arrangement of the convolutional and pooling operators and their connections and one is the numerical parameters of the operators which determine the properties of these operators like filter size and kernel size. The proposed algorithm in this paper optimizes the skeleton and the numerical parameters of the CNN architectures in a co-evolutionary scheme. The proposed algorithm is used to identify covid-19 cases via X-ray images.

Automated summary

This paper presents a genetic programming approach to optimize the structure of Convolutional Neural Networks (CNNs) for diagnosing COVID-19 cases through X-ray images. A graph representation is proposed for CNN architecture, and evolutionary operators such as crossover and mutation are designed for this representation. The algorithm optimizes both the skeleton and numerical parameters of the CNN architectures in a co-evolutionary scheme.