DFU_MultiNet: A Deep Neural Network Approach for Detecting Diabetic Foot Ulcers Through Multi-Scale Feature Fusion Using the DFU Dataset

Abstract

Diabetic foot ulcer (DFU) is a common problem among people with diabetes that can result in amputation of the affected limb. Modern DFU treatment and diagnosis methods are expensive and time-consuming. Today, the development of the computer-aided diagnosis (CAD) method makes it possible for pathologists to diagnose DFU more swiftly and accurately. This has led to a rise in interest in deep learning (DL) approaches based on CAD. In this study, we introduce a novel framework called "DFU_MultiNet," which focuses on the transfer learning approach to classify healthy and ulcer skin images using publicly available repositories. The proposed framework is developed to offer an efficient and robust method for DFU classification that determines the distinction between healthy and ulcerated skin. The proposed approach extracts features from foot samples using three well-known pre-trained CNN models: VGG19, DenseNet201, and NasNetMobile. Finally, these extracted results are merged through a summing layer to create a powerful hybrid network. Through obtaining impressive accuracy (99.06 %), precision (100.00 %), recall (98.18 %), specificity (100.00 %), F1-score (99.08 %), and AUC (99.09 %) the proposed "DFU_MultiNet" framework holds great potential as a diagnostic tool in healthcare and clinical settings.