DFUCare: deep learning platform for diabetic foot ulcer detection, analysis, and monitoring.

ABSTRACT:

Introduction

Diabetic foot ulcers (DFUs) are a severe complication among diabetic patients, often leading to amputation or even death. Early detection of infection and ischemia is essential for improving healing outcomes, but current diagnostic methods are invasive, time-consuming, and costly. There is a need for non-invasive, efficient, and affordable solutions in diabetic foot care.

Methods

We developed DFUCare, a platform that leverages computer vision and deep learning (DL) algorithms to localize, classify, and analyze DFUs non-invasively. The platform combines CIELAB and YCbCr color space segmentation with a pre-trained YOLOv5s algorithm for wound localization. Additionally, deep-learning models were implemented to classify infection and ischemia in DFUs. The preliminar

SUBMITTER: Sendilraj V 

PROVIDER: S-EPMC11460545 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature