Akciğer X-Ray Görüntülerinden COVID-19 Tespitinde Hafif ve Geleneksel Evrişimsel Sinir Ağ Mimarilerinin Karşılaştırılması

Solunum sistemine etki eden ve ileri vakalarda ölüme neden olan korona virüs salgını yaklaşık iki yıldır devam etmektedir. Her ülkenin salgın ile mücadele yöntemi farklı olmasına rağmen ortak izlenen metot ise hastalığın tespiti ve izolasyonudur. Tespit ve izolasyon için en kritik adım ise COVID-19 tanısının doğru ve hızlı konulmasıdır. Akciğer X-Ray görüntülerinde virüse özgü bulgulara rastlanılması, bu verilerin hastalık teşhisinde kullanılabileceğini göstermektedir. İlgili çalışmanın amacı, makine öğrenmesi yöntemleri ile COVID-19 ve diğer akciğer hastalıklarına ait X-Ray görüntülerini işleyerek çoklu sınıflandırma yapmaktır. Bu sayede kriz anında tanı koyma ve izolasyon için yardım alınacak alanında uzman olmayan personele mobil cihazlar vasıtasıyla karar aşamasında destek sağlanması hedeflenmektedir. Bu amaçla: COVID-19, Normal, Akciğer Opasitesi, Diğer Pnömoni etiketlerine ait 11,293 X-Ray görüntüsünden oluşan veri seti MobileNetV2, NASNetMobile, Xception ve DenseNet121 CNN ağları kullanılarak sınıflandırılmış ve sonuçlar karşılaştırılmıştır. En başarılı sonuçlar DenseNet121 ve MobileNet ağları ile elde edilmiş olup sırası ile %92,16 ve %91,78 doğruluk oranıyla sınıflandırma gerçekleştirilmiştir.

Anahtar Kelimeler:

Evrişimsel sinir ağları, Koronavirüs, COVID-19, Transfer öğrenimi, X-Ray

Comparison of Lightweight and Traditional CNN Architectures in COVID-19 Detection from Lung X-Ray Images

The corona virus epidemic, which affects the respiratory system and causes death in advanced cases, has been going on for about two years Although each country's method of fighting the epidemic is different, the common and valid method is the detection and isolation of the disease. The most critical step for detection and isolation is the correct and fast diagnosis of COVID-19. Virus-specific findings in lung X-ray images shows that these data can be used in the diagnosis of the disease. The aim of the related study is to multi classify by processing X-Ray images of COVID-19 and other lung diseases with machine learning methods. In this way, it is aimed to provide support to the personnel who are not experts in their fields, who will be helped for diagnosis and isolation during the crisis, at the decision stage through mobile devices. For this purpose: The data set consisting of 11,293 X-Ray images of COVID-19, Normal, Lung Opacity, Other Pneumonia labels was classified using the MobileNetV2, NASNetMobile, Xception and DenseNet121 CNN networks and the results were compared. The most successful results were obtained with DenseNet121 and MobileNet networks, and classification was performed with 92.16% and 91.78% accuracy rates, respectively.

Keywords:

Convolutional neural network, Coronavirus, Transfer learning, X-Ray,

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