A Novel Covid-19 Detection System Based on PSO and Hybrid Feature Using Support Vector Machines

Abstract

The world first met the coronavirus (COVID-19) in Wuhan, China in December 2019. It has continued to increase its influence from the first encounter until today. The detection of this virus, which has caused the death of many, is of great importance today. There are many approaches to the detection of this disease. One of the most effective of these approaches is the detection of COVID-19 disease using chest X-Ray images. In this paper, an intelligent system was proposed to classify normal, pneumonia patients and COVID-19 patients using chest X-Ray images. The proposed system was composed of four stage. At first, all images in the dataset were pre-processed. Then for the feature extraction uniform Local Binary Pattern (LBP) and DenseNet201 deep learning models were used. Particle swarm optimization (PSO) algorithm was used to select effective features. The determined effective features were classified by support vector machine (SVM). Accuracy and AUC parameters were used as performance criteria. Evaluated accuracy and AUC values were 99.9%, 1.00, respectively. The dataset and proposed model codes are made publicly available at: https://github.com/mfatiho/covid-detection-chest-xray