<![CDATA[Lung diseases remain a leading cause of life-threatening illnesses worldwide, particularly in developing countries with limited healthcare resources. In recent years, deep convolutional neural networks (CNNs) have demonstrated strong potential in the automatic interpretation of chest X-ray (CXR) images. However, existing approaches often rely on rigid two-stage fine-tuning or fixed-step progressive unfreezing strategies, which may fail to effectively adapt pretrained representations or destabilize optimization, especially when applied to imbalanced real-world datasets. This study proposes a validation-driven dynamic fine-tuning strategy for transfer learning that adaptively unfreezes network layers based on convergence signals observed on the validation set rather than predefined training epochs. By coupling the timing and depth of adaptation to generalization behavior, the proposed method enables controlled knowledge transfer while mitigating catastrophic forgetting and improving training stability. Experiments were conducted on a large-scale, real-world clinical dataset comprising 15,416 CXR images collected at An Giang Regional General Hospital, Vietnam. The proposed strategy was systematically evaluated across multiple CNN backbones, including Xception, DenseNet121, EfficientNetV2S, InceptionV3, MobileNet, ResNet50, and VGG16. Performance was assessed using overall accuracy and macro-F1 score to address class imbalance. Results demonstrate consistent improvements across all architectures, with a mean accuracy gain of 3.18% compared to conventional fine-tuning (p = 0.02). MobileNet achieved the best performance with 85.1% accuracy and 66.8% macro-F1, while maintaining a compact model size of 73.05 MB. These findings indicate that validation-driven dynamic fine-tuning provides a stable, statistically significant, and deployment-feasible transfer learning mechanism suitable for real-world clinical environments.]]>
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