Machine Learning-Driven Diabetic Foot Ulcer Detection with YOLOv5

Abstract

 Diabetic foot ulcers, the leading cause of non-traumatic lower limb amputations, disproportionately impact diabetic patients. Inaccurate assessment methods, time-consuming procedures, and costly therapies are only a few examples of the many ways that have been severely flawed. In order to overcome the shortcomings of existing approaches, this study introduces a deep learning framework for object detection, together with augmentation and segmentation. Both the training and testing phases make use of images from the dataset. Data augmentation is used to enhance the quantity of test and training images, which in turn reduces the frequency of false positives. Using YOLO v5, a method based on deep learning, the ulcer can be diagnosed. The ulcer's abnormality or normalcy can be determined by the suggested system. The photographs were all downsized to 640 x 480 in order to make deep learning methods more efficient and cut down on computing costs. In this case, we outperform state-of-the-art CNNs and R-CNNs by employing YOLO v5 for picture resolution.