Automated Chest X-ray Captioning and Report Generation Using CheXNet and BioGPT

Medical image captioning has emerged as a critical intersection of computer vision and natural language processing, offering significant promise for enhancing clinical radiology workflows. This paper proposes a method for generating diagnostic descriptions from chest X-ray images using advanced deep learning architectures. Three methodologies are evaluated: a conventional encoder-decoder baseline, a model integrating CheXNet with custom flow modules, and an attention-guided framework that emphasizes relevant anatomical regions. The parallel Global Flow and Context Flow pathways are designed to capture both comprehensive anatomical context and localized pathological details essential for accurate diagnosis. Evaluation results indicate that the attention-based model outperforms the alternatives, demonstrating improved capability in identifying clinically relevant features while adhering to radiological language standards. Despite these advancements, challenges remain, including dataset imbalance toward normal findings and limitations in detecting rare pathologies. These findings offer important direction for the development of AI systems that support radiological interpretation while maintaining diagnostic accuracy and clinical appropriateness.