@article{(Open Science Index):https://publications.waset.org/pdf/10011862,
	  title     = {Bayesian Deep Learning Algorithms for Classifying COVID-19 Images},
	  author    = {I. Oloyede},
	  country	= {},
	  institution	= {},
	  abstract     = {The study investigates the accuracy and loss of deep learning algorithms with the set of coronavirus (COVID-19) images dataset by comparing Bayesian convolutional neural network and traditional convolutional neural network in low dimensional dataset. 50 sets of X-ray images out of which 25 were COVID-19 and the remaining 20 were normal, twenty images were set as training while five were set as validation that were used to ascertained the accuracy of the model. The study found out that Bayesian convolution neural network outperformed conventional neural network at low dimensional dataset that could have exhibited under fitting. The study therefore recommended Bayesian Convolutional neural network (BCNN) for android apps in computer vision for image detection.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {15},
	  number    = {2},
	  year      = {2021},
	  pages     = {145 - 149},
	  ee        = {https://publications.waset.org/pdf/10011862},
	  url   	= {https://publications.waset.org/vol/170},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 170, 2021},
	}