Comparison of Hybrid Models with Multi-Feature Fusion Using Covid-19 Radiography Database

Abstract

COVID-19, which emerged in 2019 and was subsequently classified as a pandemic, has affected millions of individuals worldwide. Different variations of the illness continue to persist, even though it may seem to have subsided at the moment. Hence, it remains essential to promptly and precisely diagnose COVID-19. Chest imaging has been proven to clearly demonstrate COVID-19 infection even in the early stages of the disease, assisting physicians and radiologists in making quicker and more accurate judgements. This study proposes a hybrid model with feature fusion based on Convolutional Neural Network based models and classifiers to accurately distinguish infected patients from healthy people. The extracted features from two different Convolutional Neural Network based models are concatenated, or added before feature selection. On a publicly accessible radiography database containing 21168 images of the four classes (Covid, Lung_Opacity, Normal, and Viral Pneumonia), extensive tests utilizing five fold cross-validation have been conducted. According to the tests, an accuracy rate of about 96% has been obtained. The findings also demonstrate that the proposed approach can contribute significantly to the rapidly expanding workload in health-care systems.

Keywords

• Feature Extraction
• Feature Fusion
• Deep Learning

Covid-19 Radyografi Veritabanı Kullanılarak Çok Öznitelikli Füzyona Sahip Hibrit Modellerin Karşılaştırılması

Abstract

COVID-19, dünya çapında milyonlarca kişiyi etkiledi. Şu anda azalmış gibi görünse de hastalığın farklı varyasyonları devam ediyor. Bu nedenle, COVID-19'u hızlı ve kesin bir şekilde teşhis etmek hala hayati önem taşıyor. Göğüs görüntülemenin, hastalığın erken evrelerinde bile COVID-19 enfeksiyonunu açıkça gösterdiği, doktorların ve radyologların daha hızlı ve daha doğru kararlar almasına yardımcı olduğu kanıtlanmıştır. Bu çalışma, enfekte hastaları sağlıklı insanlardan doğru bir şekilde ayırt etmek için Evrişimsel Sinir Ağı tabanlı modellere ve sınıflandırıcılara dayalı özellik füzyonuna sahip hibrit bir model önermektedir. İki farklı Evrişimsel Sinir Ağı tabanlı modelden çıkarılan özellikler birleştirilir veya özellik seçiminden önce eklenir. Dört sınıftan (Covid, Lung_Opacity, Normal ve Viral Pneumonia) 21.168 görüntüyü içeren, kamuya açık bir radyografi veritabanında, beş kat çapraz doğrulamayı kullanan kapsamlı testler yapılmıştır. Yapılan testlere göre yaklaşık %96 oranında doğruluk oranı elde edildi. Bulgular ayrıca önerilen yaklaşımın sağlık sistemlerinde hızla artan iş yüküne önemli ölçüde katkıda bulunabileceğini göstermektedir.

Keywords

• Öznitelik Çıkarma
• Öznitelik Füzyonu
• Derin Öğrenme

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