Pixel-Based Supervised Tissue Classification of Chronic Wound Images with Deep Autoencoder

With the extensive use of machine vision methodologies, computer-assisted disease diagnosis has become a popular practice for the medical professionals. Detailed analysis of wound bed area and precise identification of the wound tissue regions are the most desirable aspects of an automated wound assessment applications. This study proposes a supervised wound tissue classification method, where a deep neural network classifier model is trained by the colour, texture and statistical features which are extracted from different tissue regions. The proposed classification process considers three types of tissue, viz. granulation (red), necrotic (black) and slough (yellow) and a total of 105 features are used for the classification. A pixel-based feature extraction approach is implemented to extract features from the tissue region, where a mask window of size \(9\,\times \,9\) runs over each pixel of the tissue regions for feature extraction. The proposed deep neural network achieves accuracy 99.997215%, sensitivity 99.998006%, specificity 99.996625% and F-Measure 99.997316%.