Catering to the widespread COVID-19 pandemic, the authors aim to develop a system based on machine learning combined with the knowledge of medical science. Considering the prevailing situation, it becomes necessary to diagnose the COVID-19 at initial stages. The idea behind the described designed model is to identify the spread of infection in patients as fast as possible. The paper sketches two different approaches: K-fold cross-validation and deep network designer which are based on deep learning technology for the prediction of COVID-19 in the initial stages by using the chest X-rays. The performance evaluation of the cross-fold validation process is compared with the designed application in the deep network designer to find an effective and efficient methodology for classification which attained better accuracy.

I want to take a minute to highlight one of the apps of Deep Learning Toolbox: Deep Network Designer. This app can be useful for more than just building a network from scratch, plus in 19a the app generates MATLAB code to programatically create networks! I want to walk through a few common uses for this app (and perhaps some not-so-common uses as well!) 1. Building a network from scratch Clearly this is one of the top benefits of Deep Network Designer, and there is a great introductory video on this topic. Click on the image to watch the short video. Link to full video 2. Importing a pretrained network and modifying it A second, popular use for the DND app is to import a pretrained network and modify it.