Wound infections, particularly Surgical Site Infections (SSIs), represent a major healthcare challenge globally, especially in low-resource settings. This project aims to develop a mobile-based machine learning model for accurately detecting SSIs from wound images by utilizing both RGB and thermal data. Building on previous research, I will explore and optimize various machine learning models, incorporating advanced techniques such as feature engineering and transfer learning. I will also evaluate the performance of these models across different computational platforms, including laptops, servers, and Android devices. The ultimate goal is to create a robust, scalable model that can be deployed widely, helping to reduce the incidence of SSIs and improve patient care worldwide.

I am participating in SuperUROP to gain in-depth research experience and apply my knowledge in machine learning to a challenging problem in healthcare. My background in computer science, particularly in image processing and AI, has prepared me well for this project. IÂ’m excited to learn more about the research process, refine my technical skills, and work toward publishing a paper.