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Globally, in 2016, one out of eleven adults suffered from Diabetes Mellitus.Diabetic Foot Ulcers (DFU) are a major complication of this disease, which ifnot managed properly can lead to amputation. Current clinical approaches to DFUtreatment rely on patient and clinician vigilance, which has significantlimitations such as the high cost involved in the diagnosis, treatment andlengthy care of the DFU. We collected an extensive dataset of foot images,which contain DFU from different patients. In this paper, we have proposed theuse of traditional computer vision features for detecting foot ulcers amongdiabetic patients, which represent a cost-effective, remote and convenienthealthcare solution. Furthermore, we used Convolutional Neural Networks (CNNs)for the first time in DFU classification. We have proposed a novelconvolutional neural network architecture, DFUNet, with better featureextraction to identify the feature differences between healthy skin and theDFU. Using 10-fold cross-validation, DFUNet achieved an AUC score of 0.962.This outperformed both the machine learning and deep learning classifiers wehave tested. Here we present the development of a novel and highly sensitiveDFUNet for objectively detecting the presence of DFUs. This novel approach hasthe potential to deliver a paradigm shift in diabetic foot care.

DFUNet: Convolutional Neural Networks for Diabetic Foot Ulcer Classification