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Journals / Muş Alparslan Üniversitesi Fen Bilimleri Dergisi / 2026 / Cilt: 14 Sayı: 1

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

Derin Öğrenme Yaklaşımları Kullanarak Göğüs Röntgeni Görüntülerinden Atelektazinin Sınıflandırılması

Ömer Faruk Gök(SIIRT UNIVERSITY, FACULTY OF ENGINEERING-ARCHITECTURE)0009-0000-1710-1631
Fatih Doganay(Mayo Clinic)0000-0003-4720-787X
Serdar Abut(Kayseri University, Department of Artificial Intelligence and Machine Learning)0000-0002-6617-6688
Pages
84–94
Publication Date
2026-06-27
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

Combining transfer learning with ROI-focused preprocessing significantly enhances atelectasis detection and shows promise as a reliable clinical decision-support approach.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:Atelectasis, the partial or complete collapse of the lung, requires early diagnosis for effective treatment.
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Key Findings & Data:Four SqueezeNet-based models were developed using two image types (raw and cropped lung regions) and two training strategies (from-scratch and transfer learning).
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Conclusion & Impact:Combining transfer learning with ROI-focused preprocessing significantly enhances atelectasis detection and shows promise as a reliable clinical decision-support approach.
🔬Design:Modeling & Simulation
📍Scope:Comprehensive Sample
⚙️Variables:Atelektazi · Derin Öğrenme · Transfer Öğrenme

Abstract

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.

Özet

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.

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Cite This Article

Gök, Ö. F., Doganay, F., Abut, S. (2026). Classification of Atelectasis from Chest X-Ray Images Using Deep Learning Approaches. Muş Alparslan Üniversitesi Fen Bilimleri Dergisi, Cilt: 14 Sayı: 1, 84–94. https://doi.org/10.18586/msufbd.1877734

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