Detection of Diabetic Foot Ulcer using Image Processing

Diabetic foot ulcers are the most exceptional accuracy, f1-score, common side effect of diabetes, and if left untreated, can result in imputations. 15% to 25% of people with diabetes worldwide have diabetic feet, and diabetes complications arise from patients’ lack of awareness of the disease’s consequences. The goal of technology leveraging is to develop unique, cost-effective, and straightforward diabetic foot diagnostic strategies for patients and physicians. This work suggests using a deep neural network model called Efficient Net for early detection and prognosis of diabetic foot ulcers. Efficient Net is applied to an image set of 844- foot images, which includes both healthy and diabetic ulcer feet. By carefully balancing network width, depth, and image resolution, Efficient Net performs better than previous models.