Transfer Öğrenme Kullanarak Göğüs Röntgeni Görüntülerinden COVID-19 Tahmini

Year 2021, Volume: 9 Issue: 4, 1395 - 1407, 31.07.2021

Kaan Bıçakcı , Volkan Tunalı

https://doi.org/10.29130/dubited.878779

Abstract

COVID-19 salgını, sadece ülkelerdeki sağlık sistemlerini değil, dünya çapında tüm toplumları birçok şekilde etkilemektedir. Bu süreçte, pandeminin üstesinden gelmek için önemli sayıda çalışma yapılmış ve birçok tıbbi teknik denenmiştir. Bu çalışmada, gerçek görüntülerden yararlanarak, bir hastada COVID-19 virüsünün olup olmadığını tahmin etmek için Evrişimsel Sinir Ağlarını göğüs röntgeni görüntülerine uyguladık. Başlangıçta, görüntü işleme alanındaki başarıları nedeniyle çok iyi bilinen mimariler olan bir dizi önceden eğitilmiş ResNet, VGG, ve Xception modellerini elimizdeki probleme uygun olarak yeniden eğitmek üzere Transfer Öğrenme kullandık. Bu modellerle ulaşılan performans tatmin edici olsa da daha isabetli ve güvenilir sonuçlar elde etmek amacıyla üç ayrı modeli bir araya getiren bir topluluk modeli oluşturduk. Son olarak, topluluk modelimiz %97'lik bir F-Skoru ile diğer tüm modellerden daha iyi performans gösterdi.

Keywords

Göğüs Röntgeni, COVID-19, Viral Pnömoni, Derin Öğrenme, Transfer Öğrenme, Topluluk Öğrenmesi

COVID-19 Prediction from Chest X-Ray Images using Transfer Learning

Abstract

The COVID-19 pandemic has been affecting our lives in many ways, not only the healthcare systems in the countries but the whole societies worldwide. Meantime, a considerable number of studies have been conducted and lots of medical techniques have been tried to overcome the pandemic. In this work, making use of real-world images, we applied Convolutional Neural Networks to chest X-ray images to predict whether a patient has the COVID-19 virus or not. Initially, we used transfer learning to fine tune a number of pre-trained ResNet, VGG, and Xception models, which are very well-known architectures due to their success in image processing tasks. While the achieved performance with these models was encouraging, we ensembled three models to obtain more accurate and reliable results. Finally, our ensemble model outperformed all other models with an F-Score of 97%.

Keywords

Chest X-Ray, COVID-19, Viral Pneumonia, Deep Learning, Transfer Learning, Ensemble Learning

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