An efficient approach for classifying chest X-ray images using different embedder with different activation functions in CNN

The automated models play an important role to identify the diseases in given chest X-ray images. This helps doctors speed up diagnosis process so that treatment of patient will start as soon possible. paper shows deep neural network model with different image embedding like SqueezeNet and Inception V3which uses four activation functions accuracy output is over 99% each model. function increases this give a comparison which functions. used classify images into binary classification COVID-19 non-COVID-19 novel corona virus great jeopardy human life 2019-20, thus earlier detection disease can slow down spreading disease.