Decision Trees for Segmentation and Assessment of Wound Healing Process of Diabetic Patients

Abstract

Wound healing is a slow process in diabetic patients due to levels of insulin variations in the body. Thus, we present an automated system to analyze and assess different stages of wound healing process of diabetic patients. The diabetic wound healing stages have been defined into three types, such as the level of tissues present in the wound: The percentage of granulations tissues, Necrotic tissues and Slough tissues present in the diabetic patients. The performance of the proposed method shall be assessed based on the clear accuracy of segmentation of wound region present in the patient body. The Decision Tree-based Segmentation method has yielded a good segmentation accuracy of 98.32% in comparison with the ground truth results of clinical data. Further, the assessment of wound healing stages of proposed method has given a good accuracy of measuring the stages of diabetic patients by measuring the percentage of different types of tissues present in the wound region. Based on the results of classification accuracy of the proposed method, we assess whether the wound is going to heal quickly or not. Thus, we presented an algorithm of Decision Trees for the purpose of segmentation and assessment of wound healing process of diabetic patients.