Classification of Pressure and Diabetic Chronic Wound Tissue Images with Deep Learning Methods

Abstract

Abstract Pressure wounds are skin injuries that occur due to prolonged inactivity of the patient. They reduce blood flow. They cause muscle and skin ischemia, which leads to necrosis and cell death. The automatic classification of pressure sores according to international principles allows rapid recommendation of appropriate treatment methods. In this way, life quality of patients will be increased by ensuring that the treatments to be applied are efficient. Diabetes foot wounds are among the current serious and chronic diseases and are among the wound types that threaten life and reduce the quality of life. In the literature, there are mostly studies that make binary classification (ulcer-nonulcer) showing which type pressure and diabetic foot ulcers belong to. In this study, the impact of recently popular deep learning methods in image processing for tissue classification of pressure and diabetic wound images is discussed. Deep Convolutional Neural Network (CNN) architectures were used to determine which of the granule, necrotic and slough classes wound images belong to, according to their characteristics on the tissue. In this study, in which 19 CNN architectures were discussed, CNN architectures that give successful results on the dataset were observed. In addition, the effect of Maximum Epoch Number (ME), Initial learning rate (LR) and Minibatch size (MBS) parameter values used in the architectures were also researched. In the experiments performed on 1045 pressure and 1045 diabetic wound images collected from different patients from the Wound Care Unit of Karaman Education and Research Hospital, the best results were obtained with the AlexNet architecture with an Accuracy value of 95.83%. Optimum values of ME, LR and MBS parameter values were also found in parameter optimization tests. Comparisons were made with the results of studies on similar wound datasets in the literature, and it was shown that AlexNet and VGG16 architectures achieved competitive results.