A Residual Neural Network with a Novel Orthogonal Regularization for Covid-19 Diagnosis using X-ray images

Year 2025, Volume: 14 Issue: 2, 240 - 246, 27.06.2025

Kazım Fırıldak , Gaffari Çelik , Muhammed Fatih Talu

https://doi.org/10.46810/tdfd.1661900

Abstract

Covid-19, solunum yollarını etkileyen ve küresel ölçekte ciddi sağlık sorunlarına yol açan viral bir enfeksiyondur. Hastalığın yüksek bulaşıcılığı nedeniyle erken teşhis ve doğru sınıflandırma büyük önem taşımaktadır. Bu çalışmada, X-ray görüntülerinden Covid-19 hastalığının tespit doğruluğunu artırmak için yeni bir ortogonal düzgünleştirme yöntemi önerilmektedir. ResNet110 kullanılarak test edilen önerilen düzgünleştirme yöntemiyle, geleneksel birimdik düzgünleştirme yaklaşımlarına göre sınıflandırma doğruluğu artırılmaktadır. Deneysel çalışmalarda, önerilen yöntem, farklı düzgünleştirme teknikleriyle karşılaştırılmış ve test doğruluk oranını %96,52 seviyesine çıkararak en yüksek sınıflama başarı oranına ulaşılmıştır. Bunun yanında önerilen yöntemin özellikle eğitim sürecinin ilerleyen aşamalarında modelin öğrenme eğrisini optimize ettiği ve test sınıflandırma doğruluğunu artırdığı gözlemlenmiştir. Ayrıca, Covid-19 tespiti için mevcut birimdik düzgünleştirme yöntemleriyle karşılaştırıldığında, önerilen yaklaşım ile test sınıflandırma başarımı doğruluk, f1score, duyarlılık, keskinlik ve özgünlük metriklerinde yaklaşık %1 oranında iyileştirme sağlanmıştır.

Keywords

Derin Öğrenme , Artık Ağlar , Ortagonal Düzgünleştirme , Covid-19

A Residual Neural Network with a Novel Orthogonal Regularization for Covid-19 Diagnosis using X-ray images

Abstract

Covid-19 is a viral infection that affects the respiratory tract and causes serious health problems on a global scale. Due to the high contagiousness of the disease, early detection and accurate classification are of great importance. In this study, a novel orthogonal regularization method is proposed to improve the detection accuracy of Covid-19 disease from X-ray images. The proposed regularization method, evaluated using ResNet110 using ResNet110, improves the classification accuracy compared to traditional Orthogonal regularization approaches. In the experimental studies, the proposed method is compared with various regularization techniques and the highest classification success rate is achieved by increasing the test accuracy rate to 96.52%. In addition, it is observed that the proposed method optimizes the learning curve of the model, especially in the later stages of the training process, and increasing the test accuracy. In addition, compared to the existing orthogonal regularization methods for Covid-19 detection, the proposed approach improved the test classification performance by approximately 1% in accuracy, F1-score, sensitivity, sharpness and specificity metrics.

Keywords

Deep Learning , Residual Network , Orthogonal Regularization , Covid-19

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