SE-ResNet18: Attention-Enhanced Deep Learning for Multi-Class Classification of Tuberculosis and Pneumonia from Chest X-ray Images

Abstract

Tuberculosis and pneumonia are major causes of respiratory mortality worldwide, requiring accurate and timely diagnosis. This study proposes SE-ResNet18, an attention-enhanced deep learning model for multi-class classification of chest X-ray images into Normal, Pneumonia, Tuberculosis, and Unknown categories. The model integrates Squeeze-and-Excitation (SE) blocks into the ResNet18 architecture to improve channel-wise feature representation. A dataset of 15,316 chest radiographs was used, split into training (13,028), validation (761), and testing (1,527) sets. Transfer learning was applied using ImageNet-pretrained weights, followed by fine-tuning for 10 epochs with the Adam optimizer (learning rate: 1×10⁻⁵). To enhance generalization, limited data augmentation (horizontal flipping and ±5° rotation) was applied only to the training set. Dropout (p = 0.4) was used in the classification head to reduce overfitting. The proposed model achieved 98.03% accuracy and a macro F1-score of 0.97 on the test set, indicating balanced performance across classes. Class-wise results were: Unknown (1.00 precision, recall, F1-score), Tuberculosis (0.95 precision, 0.99 recall, 0.97 F1-score), Pneumonia (0.98 precision, 0.96 recall, 0.97 F1-score), and Normal (0.96 precision, 0.95 recall, 0.95 F1-score). No misclassification occurred between Pneumonia and Tuberculosis. Confidence analysis showed well-calibrated predictions, with higher confidence for correct predictions (0.947) than errors (0.823), enabling identification of uncertain cases for expert review.

Keywords

• Deep learning
• Attention mechanisms
• ResNet18
• Transfer learning
• Medical image analysis

SE-ResNet18: Göğüs Röntgeni Görüntülerinden Tüberküloz ve Pnömoninin Çok Sınıflı Sınıflandırılması için Dikkat Mekanizması ile Güçlendirilmiş Derin Öğrenme Yaklaşımı

Abstract

Tüberküloz ve pnömoni, dünya genelinde solunum kaynaklı ölümlerin başlıca nedenleri arasında yer almakta olup doğru ve hızlı tanı gerektirmektedir. Bu çalışmada, göğüs röntgeni görüntülerini Normal, Pnömoni, Tüberküloz ve Bilinmeyen olarak sınıflandırmak için SE-ResNet18 adı verilen dikkat mekanizması ile güçlendirilmiş bir derin öğrenme modeli önerilmiştir. Model, kanal bazlı özellik temsillerini iyileştirmek amacıyla ResNet18 mimarisine Squeeze-and-Excitation blokları entegre edilerek geliştirilmiştir. Toplam 15.316 görüntü kullanılmış ve veri seti eğitim (13.028), doğrulama (761) ve test (1.527) olarak ayrılmıştır. Transfer öğrenme yaklaşımı kapsamında ImageNet ön-eğitimli ağırlıklar kullanılmış ve model, Adam optimizasyon algoritması (öğrenme oranı: 1×10⁻⁵) ile 10 epoch boyunca eğitilmiştir. Genelleme performansını artırmak için yalnızca eğitim verisinde sınırlı veri artırma teknikleri (yatay çevirme ve ±5° döndürme) uygulanmıştır. Aşırı öğrenmeyi önlemek amacıyla sınıflandırma katmanında dropout (p = 0.4) kullanılmıştır. Model, test kümesinde %98.03 doğruluk ve 0.97 makro F1-skoru elde etmiştir. Pnömoni ile Tüberküloz arasında yanlış sınıflandırma gözlenmemiştir. Güven analizi sonuçları, doğru tahminlerde daha yüksek güven değeri (0.947) olduğunu göstermiştir.

Keywords

• Derin öğrenme
• Dikkat mekanizmaları
• ResNet18
• Transfer öğrenme
• Tıbbi görüntü analizi

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