Automated X-Ray Chest Report Generation

Abstract

The analysis of chest X-ray images is an important aspect of the first diagnosis and screening of diseases in medicine. Yet radiologists fail to satisfactorily perform this task due to various reasons and face the difficulty of interpreting and managing these images efficiently and lead to delay in diagnosis because of high workload and the necessity for precise, fast, and consistent reports, which could affect patient outcomes. The non-automated processes involved with the creation of multiple paragraphs of free text to create a radiology report are subject to human error and time-consuming. Generating radiology reports directly from chest X-ray images by an automated, AI-powered system, potentially reducing diagnostic errors and accelerating clinical workflows. This will address the void that exists between clinical practice and research by developing an AI-powered system that autonomously generates coherent and accurate radiology reports from images obtained from chest X-rays. The most important objectives include Implementation of a convolutional neural network for feature extraction from chest X-ray images. Another could be to create free-text radiology reports up from those extracted features, using models from Natural Language Processing (NLP. Evaluation of the performance of the produced textual reports can then be done through the BLEU scoring methodology. Final preparation would be an application that could be easily deployed and used in real time by doctors, radiologists and patients to improve feedback timelines and diagnosis. The developed system is also predicted to produce clinically feasible high-quality chest X-ray reports that are medically viable. This will make the diagnosis quicker, more accessible, and accurate, in effect, making patient care better.