Detection of Ischemia in DFU to acknowledge the wound status from a far-off location

Diabetic foot ulceration (DFU) specialists have a lot of expertise in determining the presence of underlying ischemia or infection only by looking at the ulcer. We want to do the same thing with machine learning. We created a method based on image processing to substitute a visual assessment by physicians with automatic analysis of wound state for efficient wound surveillance in telemedicine situations. This project focuses on remotely monitoring DFU by uploading wound photos to doctors, obtaining DFU therapy from a remote location, and assessing the wound’s state utilizing automated methods of various image processing techniques. The presence of ischemia (inadequate blood flow) has substantial clinical consequences for DFU evaluation, which is used to predict the risk of amputation, according to DFU medical categorization systems. We introduce a new dataset and image processing methodology for detecting infection and ischemia in DFU in this paper. Wound assessment is performed by considering tissue classification as the fundamental criteria. The wound healing process involves numerous stages and various factors such as the patient’s age, gender, health status, etc. Nevertheless, it is always feasible to determine the drug’s efficacy to administer the patient under therapy by examining the wound healing process. This project aims to develop a system for monitoring the condition of the DFU that takes the wound picture as input and analyses the wound’s condition by evaluating the various intensities and characteristics present in the wound.