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dc.contributor.author | Hassan Masood, 01-131192-010 | |
dc.contributor.author | Syed Hammad Ali, 01-131192-032 | |
dc.date.accessioned | 2023-08-28T09:26:07Z | |
dc.date.available | 2023-08-28T09:26:07Z | |
dc.date.issued | 2023 | |
dc.identifier.uri | http://hdl.handle.net/123456789/16101 | |
dc.description | Supervised by Dr. Raja Muhammad Suleman | en_US |
dc.description.abstract | The rAIdiologist is an innovative project that aims to revolutionize the field of radiology by facilitating seamless collaboration among radiologists across the world. Through our comprehensive and feature-rich DICOM studio, radiologists can collaborate effectively, exchange knowledge, and enhance disease detection accuracy, particularly in chest X-rays. The primary goal of the rAIdiologist is to provide a platform where radiologists can leverage the collective expertise of their peers, regardless of geographical boundaries. By utilizing our advanced collaborative features, radiologists can add annotations, comments, and notes to medical images, fostering a collaborative environment where professionals can share insights, discuss complex cases, and collectively arrive at more accurate diagnoses. Furthermore, the rAIdiologist incorporates a robust artificial intelligence (AI) model specifically designed for disease detection in patient lung X-rays. Our AI model assists radiologists in identifying potential abnormalities and pathologies in chest X-ray images. This AI powered system not only acts as a reliable second opinion but also serves as a valuable tool for radiologists, improving efficiency and accuracy in their diagnostic processes. | en_US |
dc.language.iso | en | en_US |
dc.publisher | Software Engineering, Bahria University Engineering School Islamabad | en_US |
dc.relation.ispartofseries | BSE;P-2328 | |
dc.subject | Software Engineering | en_US |
dc.subject | Existing Software Applications | en_US |
dc.subject | Security Requirements | en_US |
dc.title | rAIdiologist | en_US |
dc.type | Project Reports | en_US |