An Advanced Healthcare Imaging System for Analyzing X-Ray Image

Abstract

In the modern healthcare landscape, medical imaging has become an essential component, providing clinicians with detailed insights into the human body's complex internal structures. Traditionally, doctors bore the sole responsibility for interpreting X-rays, a process that was not only labour-intensive but also prone to occasional errors, leading to potential misdiagnoses or delays in treatment. This study aims to enhance the interpretation of medical X-ray images by employing deep learning techniques to autonomously generate captions. Utilizing a trained transformer-based model, the system analyses incoming X-ray images to generate descriptive textual summaries of observed findings. The proposed methodology integrates image preprocessing, model inference, and user interface development to facilitate seamless engagement for healthcare professionals. Through rigorous examination and refinement, the system achieves high accuracy and reliability in caption generation, thereby improving diagnostic efficiency and patient care. Ongoing monitoring and upkeep ensure the system remains robust and adaptable to evolving healthcare requirements. Ultimately, this initiative advances AI-driven solutions in medical imaging, addressing challenges in radiology interpretation and enhancing healthcare outcomes.