CNN Based COVID-19 Prediction from Chest X-ray Images

Abstract

Coronavirus disease COVID-19 is an infectious disease caused by a newly discovered coronavirus. COVID-19 virus affects the respiratory system of healthy individuals. Chest X-ray is one of the important imaging methods to identify the coronavirus. In deep learning, a convolutional neural network (CNN), is a class of deep learning models, most commonly applied for better outcomes to analyzing visual imagery. Automated covid-19 using Deep Learning techniques could, therefore, serve as an effective diagnostic aid. In this study, we used a convolutional neural network (CNN) for detecting COVID-19 from chest X-ray images. The overall project comprises various convolutional layers. The Max-pooling layers diminish the size of the picture significantly and by joining convolutional and pooling layers, the net is able to combine its features to learn more global features of the Image. Eventually, we utilize the highlights in two completely associated (Dense) layers. Dropout is a regularization strategy, where the layer arbitrarily replaces an extent of its weights to zero for each training sample. This forces the net to learn features in an appropriate way, not depending a lot on specific weight, and thus improves speculation and 'relu' is the activation function. Applying convolutional neural network which is a Deep Learning algorithm that can take in an input image, relegate significance to different perspectives in the images and have the option to separate one from the other.