School of Computing Science, University of Hertfordshire, Hatfield, UK.
Artif Intell Med. 2023 Aug;142:102571. doi: 10.1016/j.artmed.2023.102571. Epub 2023 May 9.
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.
进化算法已成功应用于寻找许多学习算法的最佳结构，包括神经网络。由于其灵活性和有前途的结果，卷积神经网络 (CNN) 在许多图像处理应用中得到了应用。CNN 的结构极大地影响了这些算法的性能，无论是在准确性还是计算成本方面，因此，在这些网络被使用之前，找到最佳的架构是一项关键任务。在本文中，我们开发了一种遗传编程方法，用于通过 X 射线图像优化 CNN 结构，以诊断 COVID-19 病例。提出了一种用于 CNN 架构的图表示法，并专门为所提出的表示法设计了交叉和突变等进化算子。CNN 的提出的架构由两组参数定义，一组是决定卷积和池化算子的排列及其连接的骨架，另一组是算子的数值参数，它决定了这些算子的属性，如滤波器大小和核大小。本文提出的算法在协同进化方案中优化 CNN 架构的骨架和数值参数。所提出的算法用于通过 X 射线图像识别 COVID-19 病例。
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