Zatürre Tespitinde CNN Mimarilerinin Karşılaştırmalı Değerlendirilmesi: Doğrulama Kümesi Büyüklüğünün ve Batch Normalizasyonun Model Performansı Üzerindeki Etkisi

Öz

Evrişimsel sinir ağları (CNN), göğüs radyografilerinden pnömoni tespitinde yüksek doğruluk sağlayabilmekte, ancak mimari karmaşıklık ile veri kümesi özellikleri arasındaki etkileşim yeterince incelenmemektedir. Bu çalışmada SimpleCNN, DeeperCNN ve WideCNN mimarileri; çözünürlük, sınıf dengesi ve doğrulama seti büyüklüğü bakımından farklı iki veri kümesi olan PneumoniaMNIST ve Kaggle göğüs röntgeni veri kümesi üzerinde karşılaştırılmıştır. PneumoniaMNIST'te DeeperCNN %84,78 doğruluk ve 0,8366 F1 skoru elde ederken, Kaggle veri kümesinde SimpleCNN %88,30 doğruluk ve 0,9077 F1 skoru ile en yüksek performansı göstermiştir. Buna karşılık DeeperCNN, ikinci veri kümesinde %38,46 doğruluk, 1,0000 kesinlik ve 0,0154 duyarlılık ile başarısız olmuştur. Eğitim doğruluğunun %92,5'e ulaşmasına rağmen doğrulama doğruluğunun %50'de kalması, toplu normalleştirme katmanlarının eğitim ve çıkarım istatistikleri arasındaki uyumsuzluğa işaret etmiştir. Yeniden değerlendirme sonucunda ROC-AUC 0,77–0,80 aralığında korunurken doğruluk %62,5'ten %71,8'e yükselmiştir. Ayrıca 16 görüntülük doğrulama seti, test performansıyla uyumsuz model seçimine yol açmıştır. Sonuç olarak bulgular, model performansının yalnızca mimari kapasiteyle açıklanamayacağını; doğrulama setinin yeterliliği ile normalleştirme davranışının performans ve model sıralamasını belirleyen temel etmenler olduğunu ortaya koymaktadır.

Anahtar Kelimeler

• evrişimsel sinir ağları
• zatürre tespiti
• tıbbi görüntü sınıflandırma
• toplu normalleştirme
• model doğrulama

Comparative Evaluation of CNN Architectures for Pneumonia Detection: The Impact of Validation Set Size and Batch Normalization on Model Performance

Öz

Convolutional neural networks (CNNs) can achieve high accuracy in pneumonia detection from chest radiographs; however, the interaction between architectural complexity and dataset characteristics remains insufficiently investigated. In this study, SimpleCNN, DeeperCNN, and WideCNN architectures were compared on two datasets differing in resolution, class balance, and validation set size: PneumoniaMNIST and the Kaggle chest X-ray dataset. On PneumoniaMNIST, DeeperCNN achieved 84.78% accuracy and an F1 score of 0.8366, whereas SimpleCNN achieved the highest performance on the Kaggle dataset, with 88.30% accuracy and an F1 score of 0.9077. In contrast, DeeperCNN failed on the latter dataset, achieving 38.46% accuracy, 1.0000 precision, and 0.0154 recall. Although training accuracy reached 92.5%, validation accuracy remained at 50%, indicating a mismatch between training and inference statistics in the batch normalization layers. Upon reevaluation, ROC-AUC remained within the range of 0.77–0.80, while accuracy increased from 62.5% to 71.8%. Furthermore, the 16-image validation set led to model selection that was inconsistent with test performance. Overall, the findings demonstrate that model performance cannot be explained solely by architectural capacity; validation set adequacy and normalization behavior are fundamental factors influencing both performance and model ranking.

Anahtar Kelimeler

• convolutional neural networks
• pneumonia detection
• medical image classification
• batch normalisation
• model validation

Kaynakça

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