Derin Öğrenme Yaklaşımları Kullanarak Göğüs Röntgeni Görüntülerinden Atelektazinin Sınıflandırılması

Öz

Atelektazi, akciğerin kısmen veya tamamen sönmesiyle karakterize edilen ve erken tanı gerektiren önemli bir solunum sistemi hastalığıdır. Bu çalışmada, göğüs röntgeni görüntülerinden atelektazinin otomatik sınıflandırılmasında İlgi Bölgesi (ROI) kırpma ve transfer öğrenmesinin etkileri incelenmiştir. SqueezeNet mimarisi kullanılarak dört farklı model geliştirilmiştir. Modeller, iki görüntü türü (ham ve akciğer bölgesi kırpılmış görüntüler) ile iki eğitim stratejisine (sıfırdan eğitim ve transfer öğrenmesi) göre tasarlanmıştır. Model I ve II sırasıyla ham ve kırpılmış görüntüler üzerinde sıfırdan eğitilmiş, Model III ve IV ise aynı görüntü türlerinde transfer öğrenmesi kullanılarak geliştirilmiştir. Performans standart sınıflandırma ölçütleriyle değerlendirilmiştir. En yüksek performans, transfer öğrenmesi ve ROI kırpma yöntemini birleştiren Model IV tarafından elde edilmiştir. Model %98,43 doğruluk, 0,984 F1 skoru ve 0,999 AUC değeri sağlamıştır. ROI kırpmanın her iki eğitim stratejisinde de performansı artırdığı gözlenmiştir. Transfer öğrenmesi ile ROI tabanlı ön işlemenin birlikte kullanılması, atelektazi tespitinde yüksek başarı sağlayarak klinik karar destek sistemleri için umut verici sonuçlar sunmaktadır.

Anahtar Kelimeler

• Atelektazi
• Derin Öğrenme
• Transfer Öğrenme
• X-ışını Görüntüleme
• SqueezeNet

Destekleyen Kurum

TUBITAK

Proje Numarası

1059B192302217

Etik Beyan

Bu makale etik standartlara uygundur.

Teşekkür

F.D., Türkiye Bilim ve Teknolojik Araştırma Kurumu'na (TUBITAK, 2219 - Uluslararası Doktora Sonrası Araştırma Burs Programı, 1059B192302217) aldığı doktora sonrası araştırma bursu nedeniyle teşekkür etmektedir.

Classification of Atelectasis from Chest X-Ray Images Using Deep Learning Approaches

Öz

Atelectasis, the partial or complete collapse of the lung, requires early diagnosis for effective treatment. This study investigates the effects of Region of Interest (ROI) cropping and transfer learning on the automatic classification of atelectasis from chest X-ray images. Four SqueezeNet-based models were developed using two image types (raw and cropped lung regions) and two training strategies (from-scratch and transfer learning). Models I and II were trained from scratch on raw and cropped images, respectively, while Models III and IV employed transfer learning on the same image sets. Performance was evaluated using standard classification metrics. Model IV, combining transfer learning and ROI-cropped images, achieved the best performance, with 98.43% accuracy, 0.984 F1-score, and 0.999 AUC. It also reached 0.9861 precision, 0.9828 sensitivity, and 0.9859 specificity. ROI-based cropping consistently improved classification performance. Combining transfer learning with ROI-focused preprocessing significantly enhances atelectasis detection and shows promise as a reliable clinical decision-support approach.

Anahtar Kelimeler

• Atelectasis
• Deep Learning
• Transfer Learning
• X-ray Imaging
• SqueezeNet
• Image Classification

Destekleyen Kurum

the Scientific and Technical Research Council of Turkey

Proje Numarası

1059B192302217

Etik Beyan

This article complies with ethical standards

Teşekkür

F.D. acknowledges a Postdoctoral grant from the Scientific and Technical Research Council of Turkey (TUBITAK, 2219 - International Postdoctoral Research Scholarship Programme, 1059B192302217).

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