DFootNet: A Domain Adaptive Classification Framework for Diabetic Foot Ulcers Using Dense Neural Network Architecture

Abstract

Diabetic foot ulcers (DFUs) are a prevalent and serious complication of diabetes, often leading to severe morbidity and even amputations if not timely diagnosed and managed. The increasing prevalence of DFUs poses a significant challenge to healthcare systems worldwide. Accurate and timely classification of DFUs is crucial for effective treatment and prevention of complications. In this paper, we present “DFootNet”, an innovative and comprehensive classification framework for the accurate assessment of diabetic foot ulcers using a dense neural network architecture. Our proposed approach leverages the power of deep learning to automatically extract relevant features from diverse clinical DFU images. The proposed model comprises a multi-layered dense neural network designed to handle the intricate patterns and variations present in different stages and types of DFUs. The network architecture integrates convolutional and fully connected layers, allowing for hierarchical feature extraction and robust feature representation. To evaluate the efficacy of DFootNet, we conducted experiments on a large and diverse dataset of diabetic foot ulcers. Our results demonstrate that DFootNet achieves a remarkable accuracy of 98.87%, precision—99.01%, recall—98.73%, F1-score as 98.86%, and AUC-ROC as 98.13%, outperforming existing methods in distinguishing between ulcer and non-ulcer images. Moreover, our framework provides insights into the decision-making process, offering transparency and interpretability through attention mechanisms that highlight important regions within ulcer images. We also present a comparative analysis of DFootNet’s performance against other popular deep learning models, showcasing its robustness and adaptability across various scenarios.