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Analyze COVID-19 CT images based on evolutionary algorithm with dynamic searching space.
Gong, Yunhong; Sun, Yanan; Peng, Dezhong; Chen, Peng; Yan, Zhongtai; Yang, Ke.
  • Gong Y; College of Computer Science, Sichuan University, Chengdu, 610065 China.
  • Sun Y; College of Computer Science, Sichuan University, Chengdu, 610065 China.
  • Peng D; College of Computer Science, Sichuan University, Chengdu, 610065 China.
  • Chen P; Shenzhen Peng Cheng Laboratory, Shenzhen, 518052 China.
  • Yan Z; Chengdu Ruibei Yingte Information Technology Co., Ltd, Chengdu, 610054 China.
  • Yang K; Sichuan Zhiqian Technology Co., Ltd, Chengdu, 610041 China.
Complex Intell Systems ; 7(6): 3195-3209, 2021.
Article in English | MEDLINE | ID: covidwho-1406188
ABSTRACT
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.
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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Complex Intell Systems Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Language: English Journal: Complex Intell Systems Year: 2021 Document Type: Article