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

Keywords

• Covid-19
• Feature Selection
• LBP
• DenseNet
• SVM

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

Abstract

Koronavirüsle ilk olarak Aralık 2019'da Çin'in Wuhan kentinde ortaya çıkmıştır. Bugüne kadar etkisini artırmaya devam ettirmiştir. Birçok kişinin ölümüne neden olan bu virüsün tespiti günümüzde büyük önem taşımaktadır. Bu hastalığın tespiti için birçok yaklaşım bulunmaktadır. Bu yaklaşımların en başarılılarında biri göğüs röntgeni görüntüleri kullanılarak koranavirüs hastalığının veya hastalarının tespitidir. Bu çalışmada, göğüs röntgeni görüntüleri kullanılarak normal, zatürre ve koranavirüs hastalarını sınıflandırmak için akıllı bir sistem önerilmiştir. Önerilen sistem dört aşamadan oluşmaktadır. İlk olarak, veri setindeki tüm görüntüler ön işleme tabi tutulmuştur. Daha sonra özellik çıkarımı için tek tip Yerel İkili Örüntü (LBP) ve DenseNet201 derin öğrenme modelleri kullanılmıştır. Etkili öznitelikleri seçmek için parçacık sürü optimizasyonu (PSO) algoritması kullanılmıştır. Belirlenen etkin özellikler, destek vektör makinesi (SVM) ile sınıflandırılmıştır. Performans kriteri olarak doğruluk ve AUC parametreleri kullanılmıştır. Sonuç olarak, doğruluk değeri 99.9% ve AUC değeri ise 1.00 olarak bulunmuştur. Veri seti ve önerilen model kodları verilen adreste herkese açık hale getirilmiştir: https://github.com/mfatiho/covid-detection-chest-xray

Keywords

• Koronavirüs
• Özellik Seçimi
• LBP
• DenseNet
• SVM

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