L-BFGS Optimization in GANs for Enhanced Pulmonary Disease Prediction from X-ray Images

In the realm of medical imaging for pulmonary disease diagnosis, this research addresses the critical need for accurate and timely predictions from X-ray images. Pulmonary diseases pose significant health challenges, necessitating advanced methodologies for early detection and intervention. This study introduces a novel approach, "Optimizing Pulmonary Disease Prediction with GANs and L-BFGS Algorithm," which leverages Generative Adversarial Networks (GANs) in conjunction with the L-BFGS optimization algorithm. The proposed model aims to surpass existing techniques by enhancing accuracy, reducing prediction time, and enabling early detection. Through comprehensive experimentation on the MIMIC-CXR chest X-ray dataset, the model outperforms traditional methods, demonstrating superior accuracy, sensitivity, specificity, and Matthews Correlation Coefficient (MCC). Beyond its success in pulmonary disease prediction, the proposed model showcases potential applications across diverse medical imaging modalities. The study not only contributes a cutting-edge approach to pulmonary disease diagnosis but also underscores the broader implications of integrating advanced deep learning techniques into medical imaging. The insights gained from this research pave the way for more effective interventions, improved patient outcomes, and continued advancements in the field of medical image analysis for pulmonary diseases.