Radiology Report Generation Using Deep Learning and Web-Based Deployment for Chest X-Ray Analysis

The huge rise in the number of medical images has caused a major problem in radiology departments. Radiologists are now working harder than ever, which affects the quality of their diagnoses and patient care. It takes 15 to 30 minutes to write a manual radiological report for each case, and different people may see things differently. Modern departments process over 230 cases a week, which causes long delays in diagnosis. Automated report generation systems that are already in use have a lot of problems, such as not being able to be interpreted clinically, not having enough Digital Imaging and Communications in Medicine (DICOM) integration, and not having the right deployment architectures. This makes it hard for medical artificial intelligence to be widely used in clinical settings. This work shows a new automated web-based system for making radiologist reports from chest X-ray pictures using cutting-edge deep learning methods.  We suggest using a CheXNet-based convolutional neural network (CNN) with attention mechanisms and Gated Recurrent Units (GRU) to make diagnostic summaries that are useful in a clinical setting.  The system is fully compatible with DICOM and uses Streamlit, Docker, and AWS cloud services to make clinical workflows operate together smoothly. The Indiana University Chest X-ray dataset, which has 7,491 pictures and 3,955 reports, was used for training and testing.  The system did much better than the best methods available, with BLEU-1, BLEU-2, BLEU-3, and BLEU-4 scores of 0.685, 0.595, 0.533, and 0.482, respectively, as well as a METEOR score of 0.392 and a ROUGE-L score of 0.718.The deployed web application provides real-time report generation with attention heatmap visualisations enabling clinicians to understand model decision-making processes. This interpretability feature addresses critical trust barriers in clinical AI adoption whilst supporting radiologists with diagnostic assistance for routine chest imaging cases.