Diabetic foot ulcer detection using deep learning approaches

Abstract

The most recurrent side effect of diabetes is diabetic foot ulcers and if unattended cause imputations. Diabetic feet affect 15% to 25% of diabetic people globally. Diabetes complications are due to less or no awareness of the consequences of diabetes among diabetic patients. Technology leveraging is an attempt to create distinct, affordable, and simple diabetic foot diagnostic strategies for patients and doctors. This work proposes early detection and prognosis of diabetic foot ulcers using the EfficientNet, a deep neural network model. EfficientNet is applied to an image set of 844-foot images, composed of healthy and diabetic ulcer feet. Better performance is obtained compared to earlier models using EfficientNet by carefully balancing network width, depth, and image resolution. The EfficientNet performed better compared to popular models like AlexNet, GoogleNet, VGG16, and VGG19. It gave maximum accuracy, f1-score, recall, and precision of 98.97%, 98%, 98%, and 99%, respectively.