Diabetic Foot Ulcer Detection using Deep Learning Approaches

Abstract

Diabetic foot ulcers (DFUs) pose a significant threat to diabetic patients, often leading to amputations. Early and accurate detection is crucial for effective management. This research investigates the application of deep convolutional neural networks (DCNNs) for automated DFU detection from digital images. We evaluate the performance of several DCNN architectures on a large dataset of foot images, comparing their accuracy, sensitivity, and specificity against existing methods. The results demonstrate the potential of DCNNs to significantly improve the accuracy and efficiency of DFU diagnosis, facilitating timely intervention and reducing the risk of severe complications.

Introduction

Diabetic foot ulcers (DFUs) are a serious complication of diabetes, affecting millions worldwide. Delayed or inaccurate diagnosis significantly increases the risk of infection, amputation, and even mortality. Current diagnostic methods rely heavily on visual inspection by healthcare professionals, which can be subjective and prone to errors, particularly in early stages. The variability in ulcer presentation and the lack of standardized diagnostic criteria further complicate the process. Automated DFU detection using image analysis offers a promising solution, potentially improving diagnostic accuracy, speed, and accessibility, especially in resource-limited settings. This project aims to address the challenge of improving the accuracy and efficiency of DFU diagnosis.

Objectives

• To develop a deep learning-based system for accurate and efficient detection of diabetic foot ulcers from digital images.
• To evaluate the performance of the proposed system by comparing it with existing methods in terms of accuracy, sensitivity, and specificity.
• To create a user-friendly interface for clinicians to easily utilize the system for improved patient care.