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Purpose:  As to January 11, 2021, coronavirus disease (COVID-19) has caused more than 2 million deaths worldwide. Mainly diagnostic methods of COVID-19 are: (i) nucleic acid testing. This method requires high requirements on the sample testing environment. When collecting samples, staff are in a susceptible environment, which increases the risk of infection. (ii) chest computed tomography. The cost of it is high and some radiation in the scan process. (iii) chest X-ray images. It has the advantages of fast imaging, higher spatial recognition than chest computed tomography. Therefore, our team chose the chest X-ray images as the experimental dataset in this paper.  Methods:  We proposed a novel framework—BEVGG and three methods (BEVGGC-I, BEVGGC-II, and BEVGGC-III) to diagnose COVID-19  via  chest X-ray images. Besides, we used biogeography-based optimization to optimize the values of hyperparameters of the convolutional neural network.  Results:  The experimental results show that the OA of our proposed three methods are 97.65% ± 0.65%, 94.49% ± 0.22% and 94.81% ± 0.52%. BEVGGC-I has the best performance of all methods.  Conclusions:  The OA of BEVGGC-I is 9.59% ± 1.04% higher than that of state-of-the-art methods.

BEVGGC: Biogeography-Based Optimization Expert-VGG for Diagnosis COVID-19 via Chest X-ray Images