Detection of Covid-19 Virus using Supervised Machine Learning Algorithms

Abstract

Due to the continuous increase of Covid-19 infections as a global pandemic, it became necessary to detect it to avoid the damage caused by the spread of the infection. Artificial Intelligence (AI) techniques such as machine learning and deep learning have an important and effective role in the medical field applications like the classification of medical images and the detection of many diseases. In this article, we propose the use of several supervised machine learning classifiers for Covid-19 virus detection using chest x-ray (CXR) images. Five supervised classifiers are used: Support Vector Machines (SVM), Naive Bayes (NB), K-Nearest Neighbors (KNN), Logistic Regression (LR) and Artificial Neural Network (ANN). A standard dataset of 1824 CXR images are used for training and testing; 70% for training and 30% for testing. Four image embedders including Vgg16, Vgg19, SqueezeNet, and Inception-v3 are used in the experiments. Experiment results showed that most of these models achieved promising accuracy, precision, recall, and F1-scores. KNN, ANN, and LR classifiers have achieved highest classification accuracies using SqueezeNet image embedder.