Automatic Segmentation of Diabetic foot ulcer from Mask Region-Based Convolutional Neural Networks
Munoz PL, Roberto Rodríguez, Nachalie Ramos Montalvo
Abstract
Diabetic foot ulcer represents one of the leading causes of lower extremity amputation in diabetic patients. The aim of this work is to propose a computational method to carry out segmentation on diabetic foot ulcer images of patients treated with the Heberprot-P. This drug accelerates the wound healing process and reduces the risk of amputation. The used material is a bank of 1176 images provided by the Center for Genetic Engineering and Biotechnology at Havana, and as a method, we propose the use of the model of Mask R-CNN and the concept of learning transfer to automatically locate the region that delimits the ulcer. The proposed model obtained very satisfactory results, and we validated its performance together with specialist physicians, on a set of 1010 images.