Open AccessHyperparameters optimization for ResNet and Xception in the purpose of diagnosing COVID-19
Author(s) -
Hania H. Farag,
Lamiaa A. A. Said,
Mohamed R. M. Rizk,
Magdy A. Ahmed
Publication year - 2021
Publication title -
journal of intelligent and fuzzy systems
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.331
H-Index - 57
eISSN - 1875-8967
pISSN - 1064-1246
DOI - 10.3233/jifs-210925
Subject(s) - hyperparameter , computer science , convolutional neural network , artificial intelligence , residual , covid-19 , deep learning , residual neural network , machine learning , hyperparameter optimization , artificial neural network , pattern recognition (psychology) , algorithm , medicine , disease , pathology , support vector machine , infectious disease (medical specialty)
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.