<![CDATA[Chest radiography (Chest X-Ray) remains the primary imaging modality for thoracic disease diagnosis, yet remains susceptible to misdiagnosis due to anatomical complexity and overlapping pathologies. This study proposes a multi-label Deep Learning framework based on the MobileNetV2 architecture for simultaneous classification of 14 pulmonary pathologies. To address the extreme class imbalance inherent in medical datasets, a two-stage fine-tuning strategy, ColorJitter augmentation, and class weighting (pos_weight) in the Binary Cross-Entropy Loss function were implemented. Furthermore, probability threshold optimization was performed dynamically for each class using Youden’s J Statistic. Ablation study results indicate that the baseline model achieved Mean AUROC of 0.828, while the proposed method achieved Mean AUROC of 0.822. However, this marginal trade-off was strategically accepted to achieve the primary clinical objective: dramatically improving sensitivity (Recall) on critical minority pathologies, including Cardiomegaly (from 73.6% to 85.1%), Fibrosis (64.6% to 72.5%), and Hernia (71.9% to 75.0%). This framework enables simultaneous multi-label classification of 14 pulmonary pathologies using independent sigmoid activations, which inherently supports the detection of co-occurring conditions without enforcing mutual exclusivity. Consequently, the approach demonstrates enhanced clinical utility as a medical screening instrument by substantially suppressing false negative rates for high-risk pathologies. When deployed as a Computer-Aided Diagnosis (CAD) system with appropriate clinical validation, this framework demonstrates the potential to serve as a secondary screening tool in healthcare settings with limited access to specialist radiologists, particularly for detecting high-risk pathologies such as Cardiomegaly and Fibrosis.]]>
