Diabetic Foot Ulcer Identification Using Unet Variants of Transfer Learning

Abstract

Diabetic foot ulcers (DFUs) are a major complication of diabetes, affecting up to 25% of diabetic patients over their lifetime. These ulcers are often slow to heal, can lead to serious infections, and may even require amputation in severe cases. Early identification and treatment of DFUs is crucial to prevent complications and improve patient outcomes. In recent years, advances in medical imaging technology and machine learning algorithms have made it possible to accurately identify DFUs using computer vision techniques. One such technique is the use of the U-Net architecture, a deep learning algorithm that is well-suited for medical image segmentation. U-Net algorithm is a type of convolutional neural network (CNN) that is designed to perform pixel-level segmentation of images. It consists of an encoder network that extracts features from the input image and a decoder network that reconstructs the output segmentation map. The U-Net architecture has been used successfully in various medical imaging applications, including identifying skin lesions, breast tumors, and lung nodules. To apply the U-Net algorithm to DFU identification, a dataset of medical images is needed. This dataset should include both normal and ulcerated foot images, and should be labeled to indicate which pixels correspond to the ulcerated area. Once the dataset has been created, the U-Net algorithm can be trained using a supervised learning approach, where the algorithm learns to identify DFUs by comparing its segmentation predictions to the ground truth labels. One advantage of using the U-Net algorithm for DFU identification is its ability to handle images of different sizes and resolutions. This is important because medical images can vary widely in their quality and dimensions, depending on the imaging technique used.