Neural Network Classification for Iris Recognition Using Both Particle Swarm Optimization and Gravitational Search Algorithm

COVID-19 has been considered as a global pandemic. Recently, researchers are using deep learning networks for medical diseases’ diagnosis. Some of these researches focuses on optimizing deep learning neural networks for enhancing the network accuracy. Optimizing the Convolutional Neural Network includes testing various networks which are obtained through manually configuring their hyperparameters, then the configuration with the highest accuracy is implemented. Each time a different database is used, a different combination of the hyperparameters is required. This paper introduces two COVID-19 diagnosing systems using both Residual Network and Xception Network optimized by random search in the purpose of finding optimal models that give better diagnosis rates for COVID-19. The proposed systems showed that hyperparameters tuning for the ResNet and the Xception Net using random search optimization give more accurate results than other techniques with accuracies 99.27536% and 100 % respectively. We can conclude that hyperparameters tuning using random search optimization for either the tuned Residual Network or the tuned Xception Network gives better accuracies than other techniques diagnosing COVID-19.

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“…• F1 Score: is a weighted average of the true positive rate (recall) and precision and it is calculated by Equation (5).…”

Section: Xception Net Random Searchmentioning

confidence: 99%

“…Various techniques were investigated for medical imaging such as multi-criteria decision-making and entropy optimization models [3,4]. Several studies examined distinct machine/deep learning methods for medical imaging diagnosis [5][6][7]. Deep learning has gained remarkable success in computer vision for medical imaging [8].…”

Section: Introductionmentioning

confidence: 99%