IEDO-net: Optimized Resnet50 for the classification of COVID-19

Chengtian Ouyang; Huichuang Wu; Jiaying Shen; Yangyang Zheng; Rui Li; Yilin Yao; Lin Zhang; Chengtian Ouyang; Huichuang Wu; Jiaying Shen; Yangyang Zheng; Rui Li; Yilin Yao; Lin Zhang

doi:10.3934/era.2023383

Electronic Research Archive

2023, Volume 31, Issue 12: 7578-7601. doi: 10.3934/era.2023383

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IEDO-net: Optimized Resnet50 for the classification of COVID-19

1.
School of Information engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
2.
School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China
3.
College of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China

Received: 16 October 2023 Revised: 08 November 2023 Accepted: 09 November 2023 Published: 04 December 2023

The emergence of COVID-19 has broken the silence of humanity and people are gradually becoming concerned about pneumonia-related diseases; thus, improving the recognition rate of pneumonia-related diseases is an important task. Neural networks have a remarkable effectiveness in medical diagnoses, though the internal parameters need to be set in accordance to different data sets; therefore, an important challenge is how to further improve the efficiency of neural network models. In this paper, we proposed a learning exponential distribution optimizer based on chaotic evolution, and we optimized Resnet50 for COVID classification, in which the model is abbreviated as IEDO-net. The algorithm introduces a criterion for judging the distance of the signal-to-noise ratio, a chaotic evolution mechanism is designed according to this criterion to effectively improve the search efficiency of the algorithm, and a rotating flight mechanism is introduced to improve the search capability of the algorithm. In the computed tomography (CT) image data of COVID-19, the accuracy, sensitivity, specificity, precision, and F1 score of the optimized Resnet50 were 94.42%, 93.40%, 94.92%, 94.29% and 93.84%, respectively. The proposed network model is compared with other algorithms and models, and ablation experiments and convergence and statistical analyses are performed. The results show that the diagnostic performance of IEDO-net is competitive, which validates the feasibility and effectiveness of the proposed network.
- COVID-19,
- exponential distribution optimizer,
- Resnet50,
- chaotic evolution,
- rotating flight mechanism
Citation: Chengtian Ouyang, Huichuang Wu, Jiaying Shen, Yangyang Zheng, Rui Li, Yilin Yao, Lin Zhang. IEDO-net: Optimized Resnet50 for the classification of COVID-19[J]. Electronic Research Archive, 2023, 31(12): 7578-7601. doi: 10.3934/era.2023383

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Abstract

The emergence of COVID-19 has broken the silence of humanity and people are gradually becoming concerned about pneumonia-related diseases; thus, improving the recognition rate of pneumonia-related diseases is an important task. Neural networks have a remarkable effectiveness in medical diagnoses, though the internal parameters need to be set in accordance to different data sets; therefore, an important challenge is how to further improve the efficiency of neural network models. In this paper, we proposed a learning exponential distribution optimizer based on chaotic evolution, and we optimized Resnet50 for COVID classification, in which the model is abbreviated as IEDO-net. The algorithm introduces a criterion for judging the distance of the signal-to-noise ratio, a chaotic evolution mechanism is designed according to this criterion to effectively improve the search efficiency of the algorithm, and a rotating flight mechanism is introduced to improve the search capability of the algorithm. In the computed tomography (CT) image data of COVID-19, the accuracy, sensitivity, specificity, precision, and F1 score of the optimized Resnet50 were 94.42%, 93.40%, 94.92%, 94.29% and 93.84%, respectively. The proposed network model is compared with other algorithms and models, and ablation experiments and convergence and statistical analyses are performed. The results show that the diagnostic performance of IEDO-net is competitive, which validates the feasibility and effectiveness of the proposed network.

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