COVID-19 Diagnosis From X-ray Images With Artificial Neural Network Based Model

Year 2023, Volume: 26 Issue: 2, 541 - 551, 05.07.2023

Kubilay Ataş , Atakan Kaya Indrıt Myderrizi

https://doi.org/10.2339/politeknik.861536

Cited By: 4

Abstract

The number of coronavirus patients around the world is increasing day by day. Although more than one year has passed since the emergence of the disease, statistics show that the peak number of patients have not reached yet. The spread of the increase in the number of patients over time is important to prevent hospital occupancy rates from hitting dangerous levels. For this reason, people carrying the virus should be diagnosed quickly and isolated from society until the disease is over. In this study, a comprehensive artificial neural network-based model has been proposed for rapid disease diagnosis using X-ray images. Using the damage created by the coronavirus in the lung tissues, the diagnosis can be made within seconds. The model subject to study improves and augments X-ray images by pre-processing. After training is performed using DenseNet201, ResNeXt-101(32×8d), VGG-19bn and Wide-ResNet101-2 networks, Covid-19 positive or negative diagnosis is provided from the image. The best result obtained in the study is achieved by using ResNeXt-101(32×8d) network with an overall accuracy rate of 94.79%.

Keywords

Covid-19, disease diagnosis, X-ray, Artificial neural network

Yapay Sinir Ağı Tabanlı Model ile X-ray Görüntülerinden Covid-19 Teşhisi

Abstract

Dünyadaki koronavirüs hasta sayısı her geçen gün artmaktadır. Hastalığın ortaya çıkışının üzerinden bir seneden fazla zaman geçmesine rağmen istatistiklere göre henüz hasta sayısındaki zirve görülmemiştir. Hasta sayısındaki artışın zamana yayılması hastane doluluk oranlarının tehlikeli boyutlara ulaşmasını önlemek için önemlidir. Bu nedenle virüsü taşıyan bireylerin hızlıca teşhis edilerek hastalık geçene kadar toplumdan soyutlanmaları gerekmektedir. Bu çalışmada X-ray görüntüsü kullanılarak yapılabilecek hızlı hastalık teşhisi için kapsamlı bir yapay sinir ağı tabanlı model önerilmiştir. Koronavirüsün akciğerler dokularında yarattığı tahribattan yararlanılarak teşhis işlemi saniyeler içerisinde yapılabilmektedir. Çalışmaya konu olan model, X-ray görüntülerini ön-işlemlerden geçirerek iyileştirmekte ve çoğullamaktadır. DenseNet201, ResNeXt-101(32×8d), VGG-19bn ve Wide-ResNet101-2 ağları kullanılarak eğitim yapıldıktan sonra görüntüden Covid-19 pozitif veya negatif olarak teşhis konulmasını sağlamaktadır. Çalışmada elde edilen en iyi sonuç %94.79 genel doğruluk oranıyla ResNeXt-101(32×8d) ağı kullanılarak gerçekleştirilmiştir.

Keywords

Covid-19, hastalık teşhisi, X-ray, yapay sinir ağları

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