Detection of COVID-19 Cases Using Deep Learning

Authors : Muhammed Mustafa Aydın, Raşit Köker, Mehmet Demir

Pages : 1-15

View : 80 | Download : 178

Publication Date : 2025-11-30

Article Type : Research Paper

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, Yapay Zeka, Yapay Sinir Ağları, Evrişimli Sinir Ağları

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