The automated classification of thoracic pathologies using deep learning represents a promising application of Artificial Intelligence (AI) in medicine, particularly to reduce diagnostic workloads in high-intensity clinical environments. The frequent coexistence of multiple diseases within chest radiographs, combined with an inherent class imbalance in datasets such as NIH ChestX-ray14, complicates clinical decision-making and poses significant challenges for multi-label classification systems. Despite advances from established models such as CheXNet and Z-Net, limitations persist in optimising classification strategies and calibrating decision thresholds for individual pathological classes. These limitations are exacerbated by the highly imbalanced and long-tailed class distribution of the NIH ChestX-ray14 dataset, one of the most widely adopted benchmarks in the field of medical imaging.