An Automated Classification Framework for Pressure Ulcer Tissues Based on 3D Convolutional Neural Network

Pressure ulcer (PU) is a clinical pathology of localized deterioration to the underlying tissues as well as to the skin, which is generated by friction and pressure. A trustworthy diagnosis of PU, which is supported by accurate assessment, is critical to have effective therapy and save the patient's life. In this paper, we propose an automatic classification framework to segment and classify various tissues to help in diagnosis and treatment of PU. The proposed framework consists of two main stages, which are region of interest (ROI) extraction and tissue segmentation stages. The main idea is to extract various models and features from PU RGB images and supply them to multi-path 3D convolution neural network (CNN) to segment slough, necrotic eschar, and granulation tissues to help in assessing the status of PU. ROI is extracted by supplying three different color models to the CNN, which are RGB, HSV, and YCbCr. Then, the PU tissues are classified by providing four various models to the 3D CNN. These models are the original RGB image, the smoothed image with a pre-selected Gaussian kernel, and the 1st-order models of prior and current visual appearance. The framework was trained and tested on 100 color RGB PU images. The classification accuracy was evaluated using the area under the curve (AUC), the percentage area distance (PAD), and Dice similarity coefficient (DSC). The obtained preliminary results have AUC of 96%, PAD of 10%, and DSC of 93%. These experimental results are promising and can lead to an accurate assessment of the PU status.