Disease identification from chest X-ray images using deep learning

Year 2026, Volume: 31 Issue: 1 , 295 - 314 , 10.04.2026

Hatice Doymaz , Figen Ertaş

https://doi.org/10.17482/uumfd.1665390

https://izlik.org/JA63MB45SF

Abstract

Disease identification using deep learning has been an intensive area of research over the last years, and the models of which have been successfully employed in medical applications. Being of evidence from the studies conducted in recent years, the use of deep learning in medical sciences has gained more attention due to Covid-19 pandemic emerging in late 2019. In this work, ResNet50, Inceptionv3, VGG16, AlexNet, and a model designed as five-stages deep convolutional neural network cascaded with two fully connected layers, having 3.6 million parameters in total, has been used. A data set of 2500 images has been set up to consist of five classes labelled as Covid-19 (C), Bacterial Pneumonia (BZ), Viral Pneumonia (VZ), Lung Opacity (AO) and Normal (N), each of which has 500 entries randomly drawn from two different image data base. With the ResNet50 model, 95.73% accuracy, 0.9574 F1 score and 0.99672 AUC, with the Inceptionv3 model, 92.53% accuracy, 0.9251 F1 score and 0.99264 AUC, with the VGG16 model, 97.33% accuracy, 0.9734 F1 score and 0.9978 AUC, with the AlexNet model, 94.67% accuracy, 0.9487 F1 score and 0.99653 AUC, and finally with the designed model, 95.22% accuracy, 0.9521 F1 score and 0.99868 AUC value have been obtained.

Keywords

Chest X-ray images , Covid-19 , pneumonia , deep learning

DERİN ÖĞRENME İLE AKCİĞER X-RAY GÖRÜNTÜLERİNDEN HASTALIK TESPİTİ

https://izlik.org/JA63MB45SF

Abstract

Derin Öğrenme ile hastalık teşhisi son dönemlerde araştırmacıların üstünde yoğun şekilde çalıştıkları bir konu olup, yöntemleri pek çok sağlık alanında başarıyla uygulanmaktadır. Günümüzdeki birçok araştırmadan görüleceği üzere Derin Öğrenmenin tıp alanında kullanımı, 2019 yılının sonlarında ortaya çıkan ve pandemiye yol açan COVID-19 hastalığı ile daha da önem kazanmıştır. Bu çalışmada ResNet50, Inceptionv3, VGG16, AlexNet ve ayrıca 5 aşamalı evrişim ile 2 adet tam bağlantılı katman halinde tasarlanan 3,6 milyon parametreli bir model kullanılmıştır. İki ayrı veri setinden, görüntüler rastgele şekilde, her bir sınıftan 500 adet olacak şekilde, seçilip toplamda 2500 görüntü verisi kullanılarak, Covid19 (C), Bakteriyel Zatürre (BZ), Viral Zatürre (VZ), Akciğer Opaklığı (AO) ve Normal (N) olmak üzere, 5 sınıf içeren bir veri seti oluşturulmuştur. ResNet50 modeli ile %95,73 doğruluk, 0,9574 F1 skor ve 0,99672 AUC değeri, Inceptionv3 modeli ile %92,53 doğruluk, 0,9251 F1 skor ve 0,99264 AUC değeri, VGG16 modeli ile %97,33 doğruluk, 0,9734 F1 skor ve 0,9978 AUC değeri, AlexNet modeli ile %94,67 doğruluk, 0,9487 F1 skor ve 0,99653 AUC değeri, ve son olarak tasarlanan model ile %95,22 doğruluk, 0,9521 F1 skor ve 0,99868 AUC değeri elde edilmiştir.

Keywords

Akciğer X-Ray görüntüleri , Covid-19 , zatürre , derin öğrenme

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