Derin Öğrenme Kullanılarak COVID-19 Vakalarının Tespiti

Year 2025, Volume: 14 Issue: 2, 1 - 15, 30.11.2025

Muhammed Mustafa Aydın , Raşit Köker , Mehmet Demir

Abstract

2019 yılının sonlarında solunum yolu enfeksiyonu belirtileri ile ortaya çıkan COVID-19 insan hayatını ciddi şekilde etkilemekte, sağlık, eğitim, ekonomi gibi günlük sosyal aktivitelerin aksamasına sebep olmaktadır. Vakaları hızlı teşhis edebilmek salgının engellenmesi için çok önemlidir. Yapay zeka, bilgisayarların insan gibi düşünmesini sağlayarak karmaşık problemleri çözmelerini, karar vermelerini ve öğrenmelerini mümkün kılar. Derin öğrenme, yapay sinir ağları adı verilen karmaşık yapılar kullanarak, büyük miktarda verileri analiz ederek öğrenme yöntemidir. X-Ray, MRI ve BT gibi tıbbi görüntüleme yöntemlerinden sağlanan görüntüleri ve sinyal verilerini, çeşitli derin öğrenme mimarileri kullanılarak analiz edilebilmektedir. Bu çalışmada derin öğrenme modelleri kullanılarak COVID-19 vakası tespiti amaçlanmıştır. Çalışmada CNN, Xception, VGG19, Alexnet, VGG19, ResNet50 modellerinden oluşan derin öğrenme modelleri kullanılmıştır. Çalışmada 576 adet COVID-19 pozitif, 1583 adet normal, 4273 adet pnömoni teşhisi

Keywords

COVID-19 , Yapay Zeka , Yapay Sinir Ağları , Evrişimli Sinir Ağları

Project Number

1

Detection of COVID-19 Cases Using Deep Learning

Abstract

Emerging in late 2019 with respiratory infection symptoms, COVID-19 has significantly impacted human life, disrupting daily social activities such as health, education, and the economy. Rapid identification of cases is crucial for controlling the outbreak. Artificial intelligence enables computers to think like humans, allowing them to solve complex problems, make decisions, and learn. Deep learning is a method that utilizes complex structures called artificial neural networks to analyze large volumes of data and learn from them. Medical imaging techniques such as X-rays, MRIs, and CT scans can be analyzed using various deep learning architectures.
This study aims to detect COVID-19 cases using deep learning models. The models employed include CNN, Xception, VGG19, AlexNet, and ResNet50. The dataset comprises 6,432 chest X-ray images, including 576 positive for COVID-19, 1,583 normal cases, and 4,273 diagnosed with pneumonia. Of this dataset, 80% was used for training and 20% for testing. The performance of the resulting deep learning models was evaluated and compared based on accuracy, precision, sensitivity, and F1 score. The results indicate that deep learning models could significantly contribute to the detection of COVID-19 and similar diseases within health systems.

Keywords

COVID-19 , Artificial Intelligence , Artificial Neural Networks , Deep Learning , Convolutional Neural Networks.

Project Number

1

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