GSA DenseNet121 COVID 19: a Hybrid Deep Learning Architecture for the Diagnosis of COVID 19 Disease based on Gravitational Search Optimization Algorithm
In this paper, a novel approach called GSA DenseNet121 COVID 19 based on a hybrid convolutional neural network (CNN) architecture is proposed using an optimization algorithm. The CNN architecture that was used is called DenseNet121 and the optimization algorithm that was used is called the gravitational search algorithm (GSA) The GSA is adapted to determine the best values for the hyperparameters of the DenseNet121 architecture, and to achieve a high level of accuracy in diagnosing COVID 19 disease through chest x ray image analysis. The obtained results showed that the proposed approach was able to correctly classify 98% of the test set. To test the efficacy of the GSA in setting the optimum values for the hyperparameters of DenseNet121, it was compared to another optimization algorithm called social ski driver (SSD) The comparison results demonstrated the efficacy of the proposed GSA DenseNet121 COVID 19 and its ability to better diagnose COVID 19 disease than the SSD DenseNet121 as the second was able to diagnose only 94% of the test set. As well as, the proposed approach was compared to an approach based on a CNN architecture called Inception v3 and the manual search method for determining the values of the hyperparameters. The results of the comparison showed that the GSA DenseNet121 was able to beat the other approach, as the second was able to classify only 95% of the test set samples.
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