MEDICAL IMAGING FRAMEWORK FOR PNEUMONIA DETECTION

Abstract

Medical imaging has become a cornerstone of modern diagnostic healthcare, providing clinicians with non-invasive methods to identify respiratory infections like pneumonia. In clinical settings, chest X-rays are the primary tool for diagnosis; however, manual interpretation is time-consuming and prone to human error, especially in high-volume environments. This research proposes a comprehensive medical imaging framework specifically designed for automated pneumonia detection. The proposed framework integrates deep learning architectures, specifically Convolutional Neural Networks (CNNs), and image preprocessing techniques to enhance diagnostic accuracy. The system continuously processes radiographic images to identify opacities and patterns indicative of infection. In addition, the framework generates real-time diagnostic reports to assist radiologists in rapid decision-making.