Lung Nodule Classification for Clinical Decision Support Systems: A Comparative Analysis of DenseNet-121 and Xception Architectures

Öz

The early and accurate assessment of lung nodules is an important clinical requirement worldwide, as it contributes to the timely evaluation of potential malignancy risk. In this study, the performances of DenseNet-121 and Xception architectures were compared for the automatic classification of lung nodules from computed tomography (CT) images. A total of 20,432 images were evaluated in two classes, Healthy and Lung_Nodule, and both models were trained using a transfer learning approach under the same data-splitting and preprocessing conditions. DenseNet-121 showed a slight overall advantage over Xception, achieving an Accuracy of 0.894, Balanced Accuracy of 0.895, and F1-Score of 0.894. For Xception, these values were obtained as 0.892, 0.894, and 0.892, respectively. When the clinical error profiles were examined, DenseNet-121 provided higher Specificity (0.946) and a lower FPR (0.054), whereas Xception showed a small advantage in reducing missed nodule images with a lower FNR (0.152). Grad-CAM analyses indicated that both models focused more clearly on nodule regions in correctly classified images. The findings suggest that, despite their similar overall performance, the two architectures differ in terms of their clinical error behavior.

Anahtar Kelimeler

• Lung nodule
• Deep learning
• Transfer learning
• Computed tomography
• Clinical decision support system

Klinik Karar Destek Sistemleri için Akciğer Nodülü Sınıflandırması: DenseNet-121 ve Xception Mimarilerinin Karşılaştırmalı Analizi

Öz

Akciğer nodüllerinin erken ve doğru değerlendirilmesi, potansiyel malignite riskinin zamanında değerlendirilmesine katkıda bulunduğu için dünya çapında önemli bir klinik gerekliliktir. Bu çalışmada, bilgisayarlı tomografi (BT) görüntülerinden akciğer nodüllerinin otomatik sınıflandırılması için DenseNet-121 ve Xception mimarilerinin performansları karşılaştırılmıştır. Toplam 20.432 görüntü, Sağlıklı ve Akciğer_Nodülü olmak üzere iki sınıfta değerlendirilmiş ve her iki model de aynı veri bölme ve ön işleme koşulları altında transfer öğrenme yaklaşımı kullanılarak eğitilmiştir. DenseNet-121, Xception'a göre genel olarak hafif bir avantaj göstermiş ve 0,894 doğruluk, 0,895 dengeli doğruluk ve 0,894 F1 puanı elde etmiştir. Xception için bu değerler sırasıyla 0,892, 0,894 ve 0,892 olarak elde edilmiştir. Klinik hata profilleri incelendiğinde, DenseNet-121 daha yüksek özgüllük (0,946) ve daha düşük yanlış pozitif oranı (0,054) sağlarken, Xception daha düşük yanlış negatif oranı (0,152) ile kaçırılan nodül görüntülerinin azaltılmasında küçük bir avantaj gösterdi. Grad-CAM analizleri, her iki modelin de doğru sınıflandırılmış görüntülerde nodül bölgelerine daha net odaklandığını gösterdi. Bulgular, genel performansları benzer olmasına rağmen, iki mimarinin klinik hata davranışları açısından farklılık gösterdiğini düşündürmektedir.

Anahtar Kelimeler

• Akciğer nodülü
• Derin öğrenme
• Transfer öğrenme
• Bilgisayarlı tomografi
• Klinik karar destek sistemi

Kaynakça

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