ÇEKİŞMELİ ÜRETİCİ AĞLAR VE TRANSFER ÖĞRENİMİ KULLANILARAK GÖĞÜS X-RAY GÖRÜNTÜLERİNDEN COVID-19 TESPİTİ ÜZERİNE BİR DERLEME

Year 2022, , 328 - 340, 23.03.2022

Meltem Kurt Pehlivanoğlu , Uğur Kadir Arabacı

https://doi.org/10.21923/jesd.955916

Abstract

COVID-19 pandemisi ölümcül salgınlardan biridir. Hastalığın daha fazla yayılmasını azaltmak için yapay zekâya dayalı alternatif test yöntemleri değerlendirilmiştir. Viral bakteriyel zatürre (pnömoni) ile göğüs X-Ray görüntüleri COVID-19 hakkında önemli bilgiler sağlar. Bir yapay zekâ sistemi, radyologların bu göğüs röntgeni görüntülerinden COVID-19'u tespit etmesine yardımcı olabilir. Çekişmeli Üretici Ağlar (Generative Adversarial Networks-GANs) görüntü veri kümesinin genişletilmesi, yüksek çözünürlüklü görüntü elde etme, bir görüntüdeki desenin başka bir görüntüye transfer edilmesi gibi alanlarda kullanılır. Bu çalışmada, literatürde verilen göğüs X-Ray görüntüleri üzerinden COVID-19 tespiti yapan güncel çalışmalar kapsamlı olarak tartışılmıştır. Ayrıca bu çalışmalarda kullanılan veri kümelerinin özellikleri, GAN ile sentetik görüntülerin üretimi ve transfer öğrenme mimarileri üzerinde durulmuştur. Çalışma, göğüs X-Ray görüntüleri üzerinde COVID-19 tespiti yapan diğer çalışmalar için karşılaştırmalı bir rapor sağlamayı amaçlamaktadır.

Keywords

COVID-19 Tespiti, Göğüs X-Ray Görüntüleri, Makine Öğrenmesi, Derin Öğrenme, Transfer Öğrenimi

Supporting Institution

TÜBİTAK 2209-A Üniversite Öğrencileri Araştırma Projeleri Destekleme Programı

Project Number

1919B012005809 Başvuru Numaralı Proje

A SURVEY ON COVID-19 DETECTION FROM CHEST X-RAY IMAGES USING GENERATIVE ADVERSIAL NETWORKS AND TRANSFER LEARNING

Abstract

The pandemic related to the COVID-19 is one of the deadly epidemics. To reduce the further spread of the disease the alternative testing methods based on artificial intelligence have been evaluated. The chest X-Ray images with viral bacterial pneumonia provide remarkable information about COVID-19. An artificial intelligence system can help radiologists to detect COVID-19 from these chest X-Ray images. Generative Adversarial Networks (GANs) are used in areas such as expanding the image dataset (image augmentation), obtaining high-resolution images, transferring a pattern from one image to another. In this paper, the current studies which detect COVID-19 from the chest X-Ray images have been comprehensively discussed. Moreover, the properties of the datasets used in these studies, generating synthetic images with GAN and transfer learning approaches have been emphasized. This paper aims to provide a comprehensive report for other studies which detect COVID-19 from the chest X-Ray images.

Keywords

COVID-19 Detection, Chest X-Ray Images, Machine Learning, Deep Learning, Transfer Learning

Project Number

1919B012005809 Başvuru Numaralı Proje

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