GÖĞÜS RÖNTGENİ GÖRÜNTÜLERİNDE COVID-19 VE PNÖMONİ SINIFLANDIRMASI İÇIN YOLOV11 TABANLI DERİN ÖĞRENME YAKLAŞIMI

Öz

COVID-19 ve pnömoni, dünya genelinde en yaygın solunum yolu hastalıkları arasında yer almakta olup yüksek morbidite ve mortalite oranlarıyla ilişkilidir. Bu hastalıkların hızlı ve güvenilir şekilde tespit edilmesi, etkili klinik yönetim açısından kritik önem taşımaktadır. Bu çalışmada YOLOv11m modeli, göğüs röntgeni görüntülerini COVID-19, Normal ve Pnömoni olmak üzere üç kategoriye ayırmak için uygulanmıştır. Kamuya açık Kaggle veri setinden elde edilen 1.518 radyografi; COVID-19 (363), Normal (1.020) ve Pnömoni (135) olarak sınıflandırılmış ve veriler %80 eğitim, %10 test ve %10 doğrulama olacak şekilde bölünmüştür. Akciğer bölgelerini izole etmek için üç aşamalı bir ön işleme süreci kullanılmıştır: Kontrastı artırmak için CLAHE uygulanmış, Otsu eşiği ve morfolojik işlemlerle ikili akciğer maskeleri üretilmiş, ardından çıkarılan akciğer alanları %3 boşluk bırakılarak kırpılmış ve 224 × 224 piksele yeniden boyutlandırılmıştır. Model PyTorch çerçevesi kullanılarak eğitilmiştir. Performans değerlendirmesinde kesinlik, duyarlılık, F1-skoru, ROC-AUC ve Top-1/Top-5 doğruluğu gibi ölçütler kullanılmıştır. YOLOv11m modeli %98.16 Top-1 doğruluğu, %100 Top-5 doğruluğu, 0.9708 makro F1-skoru ve 0.9993 makro ROC-AUC değeri elde etmiştir. Sınıf bazlı F1-skorları COVID-19 %98.0, Normal %98.7 ve Pnömoni %94.5 olarak bulunmuştur. Sonuçlar, YOLOv11m mimarisinin göğüs radyografilerinde COVID-19, Normal ve Pnömoni örüntülerini etkili biçimde ayırt edebildiğini göstermekte ve yapay zekâ destekli klinik karar destek sistemlerinde güçlü bir potansiyel sunduğunu ortaya koymaktadır.

Anahtar Kelimeler

• göğüs radyografisi
• covid-19
• derin öğrenme
• görüntü sınıflandırma
• yolo
• Pnömoni

Destekleyen Kurum

Bu araştırma herhangi bir dış finansal destek almamıştır.

Etik Beyan

Bu araştırmada kullanılan veri seti, Kaggle platformunda kamuya açık olarak sunulan COVID-19 Radiography Database’den elde edilmiştir. Veri setindeki tüm görüntüler anonimleştirilmiş olup kişisel veri veya kimlik belirleyici bilgi içermemektedir. Araştırma sürecinde doğrudan hasta katılımı, klinik uygulama veya özel sağlık kayıtlarına erişim bulunmamıştır. Bu nedenle çalışma için etik kurul onayı ve bilgilendirilmiş onam gerekmemektedir. Çalışma, Helsinki Bildirgesi’nin etik ilkelerine uygun olarak yürütülmüştür.

A DEEP LEARNING APPROACH BASED ON YOLOV11M FOR CLASSIFYING COVID-19 AND PNEUMONIA ON CHEST X-RAY IMAGES

Öz

COVID-19 and pneumonia are among the most common respiratory diseases worldwide and are linked to high morbidity and mortality. Rapid and reliable detection is essential for effective clinical management. This study applies the YOLOv11m deep learning model to classify chest X-ray images into three categories: COVID-19, Normal, and Pneumonia. The dataset, obtained from the publicly available Kaggle repository, includes 1,518 radiographs (COVID-19: 363; Normal: 1,020; Pneumonia: 135) and is divided into training (80%), testing (10%), and validation (10%) subsets. A three-stage preprocessing pipeline was used to isolate pulmonary regions: Contrast-Limited Adaptive Histogram Equalization (CLAHE) for contrast enhancement, Otsu thresholding with morphological operations for binary lung mask generation, and cropping of lung areas with 3% padding before resizing to 224 × 224 pixels. The model was trained using the PyTorch framework. Performance evaluation included precision, recall, F1-score, ROC-AUC, and Top-1/Top-5 accuracy. YOLOv11m achieved 98.16% Top-1 accuracy, 100% Top-5 accuracy, a macro F1-score of 0.9708, and a macro ROC-AUC of 0.9993. Class-specific F1-scores were 98.0% for COVID-19, 98.7% for Normal, and 94.5% for Pneumonia. These findings demonstrate that YOLOv11m can effectively distinguish COVID-19, Normal, and Pneumonia patterns on chest radiographs, highlighting its strong potential for AI-assisted diagnostic and clinical decision-support applications.

Anahtar Kelimeler

• Chest radiography
• COVID-19
• Deep learning
• Image classification
• YOLO
• Pneumonia

Destekleyen Kurum

This research received no external funding.

Etik Beyan

This study was conducted using a publicly available dataset (COVID-19 Radiography Database, Kaggle). The dataset contains de-identified chest X-ray images and does not include any personal or identifiable patient information. Therefore, according to institutional and international research ethics guidelines, ethical approval and informed consent were not required.

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