X-Ray image-based COVID-19 detection using deep learning.

Abstract

COVID-19 is a type of respiratory infection that primarily affects the lungs. Obtaining a chest X-ray is one of the most important steps in detecting and treating COVID-19 occurrences. Our study's goal is to detect COVID-19 fromchest X-ray images using a Convolutional Neural Network (CNN). This study presentsan effective method for categorizing chest X-ray images as Normal or COVID-19 infected. We used CNN, activation functions dropout, batch normalization, and Keras parameters to build this model. The classification method was implemented usingopen source tools "Python" and "OpenCV," both of which are freely available. The acquired images are transmitted through a series of convolutional and max pooling layers activated with the Rectified Linear Unit (ReLU) activation function, and then fed into the neurons of the dense layers, and finally activated with the sigmoidal function. Thereafter, SVM was used for classification using the knowledge from the learning model to classify the images into a predefined class (COVID-19 or Normal). As the model learns, its accuracy improves while its loss decreases. The findingsof the study indicate thatall models produced promising results, with augmentation, image segmentation, and image cropping producing the most efficient results, with a training accuracy of 99.8% and a test accuracy of 99.1%. As a result, the findings show that deep features provided consistent and reliable features for COVID-19 detection. Therefore,the proposed method aids in faster diagnosis of COVID-19 and thescreening of COVID-19 patientsby radiologists.