IEDO-net: Optimized Resnet50 for the classification of COVID-19
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, th...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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AIMS Press
2023-12-01
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| Series: | Electronic Research Archive |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/era.2023383?viewType=HTML |
| _version_ | 1797390716181151744 |
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| author | Chengtian Ouyang Huichuang Wu Jiaying Shen Yangyang Zheng Rui Li Yilin Yao Lin Zhang |
| author_facet | Chengtian Ouyang Huichuang Wu Jiaying Shen Yangyang Zheng Rui Li Yilin Yao Lin Zhang |
| author_sort | Chengtian Ouyang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T23:22:18Z |
| format | Article |
| id | doaj.art-eee7f0721c8f408080bc56bc4d76687e |
| institution | Directory Open Access Journal |
| issn | 2688-1594 |
| language | English |
| last_indexed | 2024-03-08T23:22:18Z |
| publishDate | 2023-12-01 |
| publisher | AIMS Press |
| record_format | Article |
| series | Electronic Research Archive |
| spelling | doaj.art-eee7f0721c8f408080bc56bc4d76687e2023-12-15T01:40:54ZengAIMS PressElectronic Research Archive2688-15942023-12-0131127578760110.3934/era.2023383IEDO-net: Optimized Resnet50 for the classification of COVID-19Chengtian Ouyang0 Huichuang Wu 1Jiaying Shen2Yangyang Zheng 3Rui Li 4Yilin Yao5Lin Zhang61. School of Information engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China1. School of Information engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China2. School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China2. School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China2. School of Computer Science and Technology, Zhejiang Normal University, Jinhua 321004, China3. College of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, China3. College of Software Engineering, Jiangxi University of Science and Technology, Nanchang 330013, ChinaThe 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.https://www.aimspress.com/article/doi/10.3934/era.2023383?viewType=HTMLcovid-19exponential distribution optimizerresnet50chaotic evolutionrotating flight mechanism |
| spellingShingle | Chengtian Ouyang Huichuang Wu Jiaying Shen Yangyang Zheng Rui Li Yilin Yao Lin Zhang IEDO-net: Optimized Resnet50 for the classification of COVID-19 Electronic Research Archive covid-19 exponential distribution optimizer resnet50 chaotic evolution rotating flight mechanism |
| title | IEDO-net: Optimized Resnet50 for the classification of COVID-19 |
| title_full | IEDO-net: Optimized Resnet50 for the classification of COVID-19 |
| title_fullStr | IEDO-net: Optimized Resnet50 for the classification of COVID-19 |
| title_full_unstemmed | IEDO-net: Optimized Resnet50 for the classification of COVID-19 |
| title_short | IEDO-net: Optimized Resnet50 for the classification of COVID-19 |
| title_sort | iedo net optimized resnet50 for the classification of covid 19 |
| topic | covid-19 exponential distribution optimizer resnet50 chaotic evolution rotating flight mechanism |
| url | https://www.aimspress.com/article/doi/10.3934/era.2023383?viewType=HTML |
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