Comparative Analysis of Deep Learning Models for Detection of Foot Ulcer for Diabetic Patients

Diabetes is a chronic illness brought on by the body's out-of-control blood sugar levels. If detected early on, serious consequences like diabetic foot ulcers (DFUs) might be avoided. A diabetic patient's lower limb may have to be amputated due to a dangerous condition called diabetic foot ulcer (DFU). For medical professionals, diagnosing DFU can be extremely challenging because it frequently requires multiple expensive and time-consuming clinical investigations. It is possible for physicians to make more accurate and timely diagnoses in the era of massive amounts of data using deep learning. As a result, the research community has recently given the automatic identification of DFU more attention. The features of the wound and visual perception in relation to deep learning. As a result, a thorough analysis of these already-used strategies was needed. The purpose of the paper was to give researchers an overview of the state of automatic DFU identification tasks at the moment. Several conclusions have been drawn from the literature, including the necessity of using sophisticated deep learning algorithms to support physicians in reaching faster and more accurate diagnosis conclusions. The main idea in the DFU identification task can be successfully identified by an interested researcher, and this paper will assist them in completing the next research objective.