RadiXGPT is a cutting-edge deep learning project designed to automate medical reporting for radiologists. As a final year project for a 4-year Computer Science & Engineering degree, it represents the culmination of extensive research and development in data analytics, advanced data visualizations, exploratory data analysis, and data science.
At its core, RadiXGPT uses a deep learning architecture that leverages a targeted dataset of spinal patients along with captioning to automate the report generation process for CT, MRI, and spinal scans. The project is divided into several steps, including the gathering of rough data, structuring and mapping of data, and the generation of synthetic data using Stable diffusion and RGANs.
Exploratory data analysis is also a key component of the RadiXGPT project, enabling researchers to gain valuable insights into the data, identify patterns, and refine their data processing methods. The team has also employed advanced data visualization techniques to help communicate their findings and insights to others.
To develop an organized recipe for the deep learning architecture, the team has utilized cutting-edge data science concepts, including image captioning classification, concept detection, and image captioning generation. By combining these techniques, the RadiXGPT project is able to produce highly accurate and detailed reports that are tailored to each individual patient.
Finally, the project includes an ETL (Extract, Transform, Load) process that ensures the report generated is at the radio logical level. This final step ensures that radiologists can quickly and accurately assess the results and provide the necessary treatment recommendations.
Overall, RadiXGPT is an impressive example of the potential for deep learning and data science to revolutionize medical reporting and improve patient outcomes. The project's Github repository provides detailed information on each step of the process, including the code used and the results obtained, making it an excellent resource for anyone interested in this exciting field.
It i divided into steps as: 1-Gather Rough data 2-Structuring and mapping data 3-Generate Synthetic data using Stable diffusion and RGANs 4-Exploratory Data analysis 5-Data science 6-Image captioning classification 7-Concept detection 8-Image captioning Generation 9-ETL for Radioological level report generation