# A Systematic Overview of Recent Methods for Non-Contact Chronic Wound Analysis
## 5 Tissue Classification
The basic principle is that the user sends a chronic wound image to Tele-Medical Hub, where it is saved on the server and further analyzed. Performance analysis was performed on 50 wound images from the Medetec database. The system uses k-means clustering and fuzzy c-means to segment the image with an accuracy of 93.45% and 96.25% respectively, while tissues were classified with Bayesian classifier with an accuracy of 87.11%. Inside the system, a medical expert can easily access the patient's wound data, track the progress of the wound healing, or even advise the patient to change the wound healing treatment. Patient clinical information and wound images are linked and globally accessible through a web application [89]. In similar research by the same authors [PERSON] et al. [90] and [PERSON] [7], segmentation was based on fuzzy c-means and particle swarm optimization (PSO), respectively, while tissues were classified in both papers with linear discriminant analysis (LDA). The segmentation accuracy of fuzzy c-means was 98.98% and PSO 98.93%, while the classification accuracy of LDA in [90] was 91.45% and in [7] 98%. In most recent research by [PERSON] [11], classification was evaluated with four different algorithms, LDA with an accuracy rate of 85.67%, decision trees 84.29%, naive Bayes 78.66%, and random forest, which showed a high accuracy rate of 93.75%. In all papers, images from the Medetec database were taken alongside with captured images of the Indian population. Another smartphone-based method was presented by [PERSON] et al. [55] and furthermore [PERSON] et al. [34] where segmentation was performed with an augmented mean shift algorithm and classification with a RYKW color model and k-means clustering. In their most recent paper, [PERSON] et al. [46] used a superpixel SVM classifier which was trained in two stages combining color and texture features. Mean color, color histogram, and wavelet texture feature showed the best for separation between wound and surrounding skin, while dominant color, color histogram, and bag of words (BoW) histogram showed the best results for background and foot tissue. On acquired 100 foot ulcer images average sensitivity was 73.3% and specificity 94.6%. The problem with the mentioned work is that images can be captured only with a box specialized for this purpose, under controlled light, and designed only for feet which limits the method's robustness and general application. [PERSON] et al. [33] proposed two different 3D CNN architectures, one for segmentation (region of interest (ROI) extraction), and one for tissue classification. Each one has different input images (models) with its own pathways that connect in fully connected layers at the end of the network. Input images for proposed wound segmentation use HSI and Grayscale color spaces, while for tissue classification linear combinations of discrete Gaussians (LCDG) image and prior visual appearance image are added as separate inputs to the network. The CNN's were trained and tested on 193 pressure ulcer images with a DSC of 95% for wound segmentation and 92% for tissue classification. In similar research by [PERSON] et al. [43] RGB image is added as an input to the CNN alongside with prior models and obtained tissue classification showed DSC accuracy of 92%. In another research by [PERSON] et al. [91] for the purpose of the segmentation, the YCbCr image was added as an input model to the CNN, and in the classification network, the RGB image was used instead of the HSI image. This architecture gave DSC of 93% on 100 images obtained from IGURCO GESTON S. L. healthcare services company and Medetec database. On a database from the same institution, another experiment was carried out in [PERSON] et al. [92] where LCDG-pixel-wise probability is estimated from the grayscale image and the color probability is estimated from the color image with the Euclidean distance. Initial tissue classification is performed using vector element with the highest probability.