Classification of Covid-19 Based on a Combination of GLCM and Deep Features by Using X-Ray Images

Year 2022, Volume: 10 Issue: 1, 313 - 325, 10.03.2022

Tolga Hayıt Gökalp Çınarer

https://doi.org/10.33715/inonusaglik.1015407

Cited By: 2

Abstract

With the coronavirus epidemic (Covid-19) affecting the whole world, urgent but accurate and fast diagnostic methods have been needed for viral diseases such as Covid-19. With the emergence of Covid-19, lung tomography and X-Ray images have been begun to be used by medical doctors to detect Covid-19. It is known that traditional and modern machine learning approaches using X-Ray and tomography images are used for disease diagnosis. In this respect, applications based on artificial intelligence contribute to the sector by showing similar or even better performances to field experts. In this study, for disease diagnosis by using X-Ray lung images, a hybrid support vector machines (SVM) classification model based on the combination of deep and traditional tissue analysis features is proposed. The dataset has been used consists of lung images of healthy, Covid-19, viral pneumonia and lung opacity patients. Hybrid features obtained from X-Ray images have been obtained by using Gray Level Co-occurrence Matrix (GLCM) and DenseNet-201 deep neural network. The performance of hybrid features has been compared to GLCM features as a traditional approach. Both attributes have been trained with SVM. An average of 99.2% accuracy has been achieved in classification success. Other performance measures which have been obtained show that hybrid features are more successful than the traditional method. The proposed artificial intelligence-based method for the diagnosis of Covid-19 has been shown to be promising.

Keywords

Artificial intelligence, Covid-19, Deep learning, Hybrid model

X-RAY GÖRÜNTÜLERİNİ KULLANARAK GLCM VE DERİN ÖZNİTELİKLERİN BİRLEŞİMİNE DAYALI COVID-19 SINIFLANDIRILMASI

Abstract

Koronavirüs salgınının (Covid-19) tüm dünyayı etkisi altına alması ile Covid-19 gibi viral hastalıklar için acil ancak doğru ve hızlı teşhis yöntemlerine ihtiyaç duyulmuştur. Covid-19’un ortaya çıkması ile birlikte Covid-19’un tespit edilmesi için tıp doktorları tarafından akciğer tomografi ve X-Ray görüntüleri kullanılmaya başlanmıştır. Geleneksel ve modern makine öğrenimi yaklaşımlarının X-Ray ve tomografi görüntüleri kullanılarak hastalık teşhisi için kullanıldığı bilinmektedir. Bu yönü ile yapay zekaya dayalı uygulamalar alan uzmanlarına benzer ve hatta neredeyse daha iyi performanslar ortaya koyarak sektöre katkı sağlamaktadır. Bu çalışmada X-Ray akciğer görüntüleri kullanılarak hastalık teşhisi için derin ve geleneksel doku analizi özniteliklerinin kombinasyonuna dayalı hibrit bir destek vektör makineleri (SVM) sınıflandırma modeli önerilmektedir. Çalışmada kullanılan veri seti, sağlıklı, Covid-19, viral pnömoni ve akciğer opasitesi hastalarının X-Ray akciğer görüntülerinden oluşmaktadır. X-Ray görüntülerinden elde edilen hibrit öznitelikler Gri Seviye Eş-Oluşum Matrisi (GLCM) ve DenseNet-201 derin sinir ağı kullanılarak elde edilmiştir. Hibrit özniteliklerin performansı, geleneksel bir yaklaşım olarak GLCM öznitelikleri ile karşılaştırılmıştır. Her iki öznitelik SVM ile eğitilmiştir. Sınıflandırma başarısında ortalama %99.2 doğruluk değerine ulaşılmıştır. Elde edilen diğer performans ölçütleri de hibrit özniteliklerin geleneksel yönteme göre daha başarılı olduğunu göstermektedir. Covid-19 teşhisi için önerilen yapay zekâ tabanlı yöntemin umut verici olduğu görülmüştür.

Keywords

Covid-19, Derin öğrenme, Hibrit model, Yapay zekâ

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