What question did this study set out to answer?

To enhance lung disease classification from chest X-ray images using a hybrid CNN-tree framework.

February 28, 2026Bioengineering0 citationsOpen Access

Improving Tree-Based Lung Disease Classification from Chest X-Ray Images Using Deep Feature Representations

AAAbdulaziz A. Alsulami QAQasem Abu Al-Haija RARayed Alakhtar

Key Points

To enhance lung disease classification from chest X-ray images using a hybrid CNN-tree framework.
Merged four chest X-ray datasets into a five-class dataset.
Fine-tuned a ResNet-18 model to extract deep feature representations.
Applied Principal Component Analysis to reduce dimensionality and redundancy.
Utilized Synthetic Minority Over-sampling Technique to address class imbalance.
Trained Decision Tree, Random Forest, and XGBoost classifiers on the processed features.
Achieved weighted F1-scores between 0.977 and 0.982 in five-fold cross-validation.
Demonstrated significant reduction in inter-class confusion among categories.
Provided low inference latency for energy-efficient deployment.

Abstract

Healthcare systems worldwide face increasing pressure to deliver accurate, affordable, and scalable diagnostic services while maintaining long-term sustainability. Chest X-ray screening is considered one of the most cost-effective methods for detecting lung disease. However, many deep learning approaches are computationally intensive and difficult to interpret, which limits their adoption in high-throughput, resource-constrained clinical settings. This study proposes a hybrid CNN–tree framework for automated lung disease classification from chest X-ray images, which targets COVID-19, pneumonia, tuberculosis, lung cancer, and normal cases. To ensure robustness and generalization, four publicly available chest X-ray datasets from different sources are merged into a unified five-class dataset, which introduces realistic variations in imaging conditions and patient populations. A ResNet-18 model is fine-tuned to extract domain-specific deep feature representations. Feature dimensionality and redundancy are reduced using Principal Component Analysis, while class imbalance is addressed through the Synthetic Minority Over-sampling Technique. The resulting compact feature vectors are used to train interpretable tree-based classifiers, which include Decision Tree, Random Forest, and XGBoost. Experiments conducted using five-fold stratified cross-validation demonstrate substantial and consistent performance gains. When trained on fine-tuned and preprocessed deep features, all evaluated tree-based classifiers achieve weighted F1-scores between 0.977 and 0.982 using five-fold cross-validation, with a significant reduction in inter-class confusion. In addition, the proposed framework maintains low per-sample inference latency, which supports energy-efficient and scalable deployment. These results indicate that combining deep feature learning with interpretable tree-based models provides a practical and reliable solution for sustainable chest X-ray screening in real-world clinical environments.

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