Use of Chest X-ray Images and Artificial Intelligence Methods for Early Diagnosis of COVID-19

Year 2025, EARLY VIEW, 1 - 1

Maral A. Mustafa , O. Ayhan Erdem , Esra Söğüt

https://doi.org/10.2339/politeknik.1654887

Abstract

The worldwide epidemic brought on by COVID-19 has substantially hurt people’s health. To discover and treat ill people, given the significant usage of efficient screening and diagnostic methods, as well as a crucial way to this deadly illness. One strategy that might be used to help with COVID-19 early diagnosis is to make use of X-ray pictures of individuals’ chests. Different Computer Aided Diagnosis (CAD) methods have been created to aid doctors in doing this work by providing them more extra information and suggestions. This investigation uses pictures of chest X-rays taken to create a CAD method for COVID-19 illness. Convolutional Neural Network (CNN), Resnet50, Xception, Densnet, Mobilenet, VGG16, Resnet152v2, and Inceptionv3 will use in the investigation to examine the pictures and remark on automatic detection and categorization of COVID-19 cases. The effectiveness of each method will be examined on a big collection of chest X-ray pictures to identify its accuracy and reliability in detecting COVID-19 cases. The result of this investigation could be used to design an effective and reliable tool for COVID-19 diagnosis and evaluation.

Keywords

Covid-19 classification, VGG16, resnet, inception, CNN

COVID-19’un Erken Teşhisi için Göğüs Röntgeni Görüntülerinin ve Yapay Zeka Yöntemlerinin Kullanımı

Abstract

COVID-19'un yol açtığı küresel salgın, insan sağlığı üzerinde önemli olumsuz etkilere neden olmuştur. Bu ölümcül hastalığın tespiti ve tedavisi için etkili tarama ve tanı yöntemlerinin yaygın kullanımı büyük önem taşımaktadır. COVID-19'un erken teşhisine yardımcı olabilecek stratejilerden biri, hastaların göğüs röntgeni (X-ray) görüntülerinden faydalanmaktır. Doktorlara ek bilgi ve öneriler sağlayacak ve hastalık teşhis sürecini destekleyecek çeşitli Bilgisayar Destekli Tanı (BDT) yöntemleri geliştirilmekte ve kullanılmaktadır. Bu çalışma COVID-19 hastalığının teşhisini gerçekleştirmek üzere göğüs röntgeni görüntülerini temel alan bir BDT yöntemi geliştirmeyi amaçlamaktadır. Görsellerin analiz edilmesi ve COVID-19 vakalarının otomatik tespitine ve sınıflandırılmasına yönelik değerlendirme yapılabilmesi için Evrimsel Sinir Ağları (ESA), ResNet50, Xception, DenseNet, MobileNet, VGG16, ResNet152v2 ve Inceptionv3 gibi modeller kullanılmaktadır. Her yöntemin etkinliği geniş ölçekli bir göğüs röntgeni veri kümesi üzerinde test edilmekte ve COVID-19 vakalarının tespitindeki doğruluk ve güvenilirlik durumları değerlendirilmektedir. Bu çalışmadan elde edilen sonuçlar, COVID-19'un teşhis ve değerlendirilmesine yönelik etkili ve güvenilir bir tanı aracı geliştirilmesi için temel oluşturacaktır.

Keywords

Covid-19 sınıflandırılması, VGG16, resnet, inception, CNN

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