Multi-Objective RSO-Based Convolutional Neural Networks
Version 1.0.0 (40,1 Mo) par
Gehad Ismail Sayed
RSO is used to find the optimal values for the hyperparameters of the deep-learning Architecture
Rat Swarm Optimizer (RSO) is one of the recently published swarm intelligence algorithms proposed in late 2020 by G. Dhiman. This paper introduces a novel diagnosis approach, namely RSO-AlexNet-COVID-19. The proposed hybrid approach is based on the rat swarm optimizer (RSO) and convolutional neural network (CNN). RSO is used to find the optimal values for the hyperparameters of the AlexNet Architecture to achieve a high level of diagnostic accuracy for COVID-19. It obtained overall classification accuracy of 100% for CT images datasets and an accuracy of 95.58% for the X-ray images dataset. Moreover, the performance of the proposed hybrid approach is compared with other CNN architecture, Inception v3, VGG16, and VGG19.
Citation pour cette source
Gehad Ismail Sayed A Novel Multi-Objective Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease. Aut. Control Comp. Sci. 56, 198–208 (2022). https://doi.org/10.3103/S0146411622030075
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Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease
Rat Swarm Optimizer-Based Convolutional Neural Networks for the Diagnosis of COVID-19 Disease
Version | Publié le | Notes de version | |
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1.0.0 |