Foot Check: Advance Detection of Diabetic Ulcer Using AI-Driven Image and Text Processing

Authors- Preethiga D, Sathya D, Sathasri M, Mr.S.Baskar

Abstract-Diabetic ulcers are a serious complication of diabetes that can lead to infections and amputations if not detected and treated early. Current diagnostic methods rely heavily on manual evaluation, which is subjective and error-prone. This project proposes an AI-based system for the early diagnosis and classification of diabetic ulcers using deep learning for image analysis and Natural Language Processing (NLP) for clinical text evaluation. Convolutional Neural Networks (CNNs) will classify ulcer images by severity, while Transformer-based NLP models will analyze clinical notes and patient histories for contextual insights. By integrating these modalities, the system aims to enhance diagnostic accuracy and provide healthcare professionals with a reliable tool for timely intervention. This AI system not only automates ulcer detection but also supports decision-making, reducing the burden on medical staff and improving patient outcomes. It offers a comprehensive understanding of a patient’s condition, facilitating personalized treatment and remote monitoring, especially for those in underserved areas. Ultimately, this innovative approach seeks to transform diabetic ulcer care, improving healthcare efficiency and accessibility.

DOI: /10.61463/ijset.vol.13.issue2.200