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.

An evolutionary ensemble learning for diagnosing COVID-19 via cough signals.

Objective The spread of the COVID-19 disease has caused great concern around the world and detecting the positive cases is crucial in curbing the pandemic. One of the symptoms of the disease is the dry cough it causes. It has previously been shown that cough signals can be used to identify a variety of diseases including tuberculosis, asthma, etc. In this paper, we proposed an algorithm to diagnose via cough signals the COVID-19 disease. Methods The proposed algorithm is an ensemble scheme that consists of a number of base learners, where each base learner uses a different feature extractor method, including statistical approaches and convolutional neural networks (CNN) for automatic feature extraction. Features are extracted from the raw signal and some transforms performed it, including Fourier, wavelet, Hilbert-Huang, and short-term Fourier transforms. The outputs of these base-learners are aggregated via a weighted voting scheme, with the weights optimised via an evolutionary paradigm. This paper also proposes a memetic algorithm for training the CNNs in the base-learners, which combines the speed of gradient descent (GD) algorithms and global search space coverage of the evolutionary algorithms. Results Experiments were performed on the proposed algorithm and different rival algorithms which included a number of CNN architectures in the literature and generic machine learning algorithms. The results suggested that the proposed algorithm achieves better performance compared to the existing algorithms in diagnosing COVID-19 via cough signals. Conclusion This research showed that COVID-19 could be diagnosed via cough signals and CNNs could be employed to process these signals and it may be further improved by the optimization of CNN architecture.