Yeni Corona virüs hastalığının önceden eğitilmiş evrişimli sinir ağları kullanılarak göğüs röntgen görüntülerinden tespiti

Yıl 2021, Cilt: 36 Sayı: 4, 2095 - 2108, 02.09.2021

Ali Narin , Yalçın İşler

https://doi.org/10.17341/gazimmfd.827921

Cited By: 4

Öz

COVID-19 virüsü özellikle yaşlı bireyleri ve kronik rahatsızlığı bulunan hastaları ciddi bir şekilde etkileyen ve ölümlere sebep olmaktadır. Hızlı ve doğru bir erken teşhis ölüm oranını düşürmede ve bu salgının ekonomik maliyetini azaltmada anahtar bir role sahiptir. Bu amaçla, teşhis kitleri, teşhis aletleri ve tıbbi görüntüleme kullanılarak teşhis gibi yöntemler geliştirilmiştir. Her ne kadar bunlar arasında bilgisayarlı tomografi ile elde edilen göğüs görüntüleri altın bir standart olarak kabul edilse de, bu cihaza erişimde genellikle sorun yaşanmaktadır. Bu nedenle, röntgen cihazı gibi daha kolay ulaşılabilen cihazlar yardımıyla teşhis konulması oldukça önemlidir. Kaggle tarafından sunulan ve göğüs röntgen görüntülerinden oluşan “COVID-19 radiography database” veri tabanı bu çalışmada kullanılmıştır. Üç farklı ResNet modeli (ResNet 50, ResNet 101 ve ResNet 152) (a) COVID-19 hastalarının sağlıklı bireylerden ayırt edilmesi, (b) COVID-19 hastalarının zatürre hastalarından ayırt edilmesi ve (c) COVID-19 hastalarının zatürre hastaları ve sağlıklı bireylerden ayırt edilmesi için denenmiştir. Bu modeller arasında en yüksek başarılı sonuçları ResNet 50 modeli vermiştir. Elde edilen sonuçlara göre, COVID-19 hastalarının sağlıklı bireylerden ayırt edilmesinde %99,3 başarıya, COVID-19 hastalarının zatürre hastalarından ayırt edilmesinde %99,2 başarıya ve COVID-19 hastalarının hem normal bireylerden hem de zatürre hastalarından ayırt edilmesinde %97,3 başarıya ulaştık. Bu sonuçlar bildiğimiz kadarıyla sadece röntgen görüntüleri kullanılarak COVID-19 teşhisinde elde edilen en yüksek sınıflandırıcı başarımlarıdır. Sonuç olarak, önceden eğitilmiş ResNet 50 modeli COVID-19 hastalarının sadece göğüs röntgen görüntülerinden hızlı ve doğru bir şekilde tespit edilmesinde büyük bir potansiyele sahiptir. Röntgen cihazları sağlık kuruluşlarında diğerlerine kıyasla nispeten daha kolay erişilebilir cihazlar olduğundan, bu çalışmada kullanılan modelin bu salgını yenme konusunda yardımcı olacağına inanıyoruz.

Anahtar Kelimeler

Teşhis, COVID-19, Corona virüs, Röntgen görüntüleri, Derin öğrenme, ResNet modelleri

Detection of new coronavirus disease from chest x-ray images using pre-trained convolutional neural networks

Öz

The COVID-19 virus has affected seriously and caused death especially for older people and patients with chronic diseases. Rapid and accurate early diagnosis has a key role to reduce the mortality and to decrease the economic cost of this pandemic. For this purpose, diagnostic kits, diagnostic aids, and diagnosis using medical imaging methods have been investigated. Although the chest imaging using Computed Tomography (CT) has been accepted as a golden standard among them, there is big challenge to reach this equipment in general. Hence, the diagnosis using more accessible devices like X-rays is very crucial. Kaggle’s chest X-ray images called the “COVID-19 radiography database” were used in this study. Three different ResNet models (ResNet 50, ResNet 101, and ResNet 152) were investigated (a) to discriminate patients with COVID-19 from normal subjects, (b) to discriminate patients with COVID-19 from patients with Pneumonia, and (c) to discriminate patients with COVID-19, patients with Pneumonia, and normal subjects. ResNet 50 model gave the highest performances among these three models. As a result, we achieved the accuracy of 99.3% to discriminate COVID-19 and Normal, the accuracy of 99.2% to discriminate COVID-19 and Pneumonia, and the accuracy of 97.3% to discriminate COVID-19, Normal, and Pneumonia. In our knowledge, these results are the highest classification accuracies in the literature in diagnosing COVID-19 using x-ray images only. In conclusion, the pre-trained ResNet 50 model has a big potential to detect the patients with COVID-19 quickly and accurately using chest X-Ray images only. Since X-ray devices are relatively more accessible devices in health organizations, we believe that the model used in this study may help defeating this pandemic.

Anahtar Kelimeler

Diagnosis, COVID-19, Corona virus, x-ray images, deep learning, ResNet models

Kaynakça

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