Gong Yunhong, Sun Yanan, Peng Dezhong, Chen Peng, Yan Zhongtai, Yang Ke
College of Computer Science, Sichuan University, Chengdu, 610065 China.
Shenzhen Peng Cheng Laboratory, Shenzhen, 518052 China.
Complex Intell Systems. 2021;7(6):3195-3209. doi: 10.1007/s40747-021-00513-8. Epub 2021 Sep 6.
The COVID-19 pandemic has caused a global alarm. With the advances in artificial intelligence, the COVID-19 testing capabilities have been greatly expanded, and hospital resources are significantly alleviated. Over the past years, computer vision researches have focused on convolutional neural networks (CNNs), which can significantly improve image analysis ability. However, CNN architectures are usually manually designed with rich expertise that is scarce in practice. Evolutionary algorithms (EAs) can automatically search for the proper CNN architectures and voluntarily optimize the related hyperparameters. The networks searched by EAs can be used to effectively process COVID-19 computed tomography images without expert knowledge and manual setup. In this paper, we propose a novel EA-based algorithm with a dynamic searching space to design the optimal CNN architectures for diagnosing COVID-19 before the pathogenic test. The experiments are performed on the COVID-CT data set against a series of state-of-the-art CNN models. The experiments demonstrate that the architecture searched by the proposed EA-based algorithm achieves the best performance yet without any preprocessing operations. Furthermore, we found through experimentation that the intensive use of batch normalization may deteriorate the performance. This contrasts with the common sense approach of manually designing CNN architectures and will help the related experts in handcrafting CNN models to achieve the best performance without any preprocessing operations.
新冠疫情引发了全球警报。随着人工智能的发展，新冠病毒检测能力得到了极大提升，医院资源也得到了显著缓解。在过去几年中，计算机视觉研究主要集中在卷积神经网络（CNN）上，它能显著提高图像分析能力。然而，CNN架构通常是由丰富的专业知识手动设计的，而这些知识在实践中很稀缺。进化算法（EA）可以自动搜索合适的CNN架构，并自动优化相关超参数。通过EA搜索得到的网络可用于有效处理新冠病毒计算机断层扫描图像，无需专家知识和手动设置。在本文中，我们提出了一种基于EA的新颖算法，该算法具有动态搜索空间，可在病原体检测之前设计用于诊断新冠病毒的最优CNN架构。我们在新冠CT数据集上针对一系列先进的CNN模型进行了实验。实验表明，基于EA的算法搜索得到的架构在没有任何预处理操作的情况下取得了最佳性能。此外，我们通过实验发现，过度使用批量归一化可能会降低性能。这与手动设计CNN架构的常识性方法形成对比，将有助于相关专家在不进行任何预处理操作的情况下手工制作CNN模型以实现最佳性能。
