## 2 Related Work  
Over the years, many studies have focused on automatic wound assessment methods to diminish possible complications. The application of fast and accurate systems that attempt to solve this problem has increased with technological advances and the growth of computational power. Despite the popularity growth in DL applications, conventional ML algorithms continue to be studied, mainly due to their more straightforward nature and higher model interpretability. A vast number of image processing methods for wound segmentation using DL are discussed next. A system based on DL to calculate the area of wound surfaces was proposed by [7]. The image segmentation step was performed using a convolutional neural network (CNN) variant, the convolutional encoder-decoder, to segment the wound region from the background in an end-to-end style. An automatic segmentation using a pre-trained fully convolutional neural network model in a pixel-wise manner, where each pixel is attributed to a class, was proposed by [8]. Even with the promising results, this segmentation method is less accurate when distinguishing small wounds and has the tendency to draw smooth contours, which is incompatible with the irregular nature of the wound's borders. A two-tier transfer learning was used to be more effective, with the CNN models trained on the ImageNet dataset [9] and Pascal VOC dataset. Multiple studies developed a system built upon a MobileNet framework [10; 11; 12]. [PERSON] et al. proposed an efficient and accurate framework named WoundSeg, based on an adapted MobileNet architecture with different numbers of channels along with VGG-16 as a baseline [11]. One major drawback of this developed annotation tool is that it was based on a watershed algorithm; hence, the model is learning the watershed annotations instead of the annotations from a specialist. After the segmentation, a post-processing step based on traditional methods removes the background of the images and corrects the segmentation results by removing small noise objects and filling the inner holes from the segmentation masks. Other frameworks, such as Unet, Segnet, and LinkNet, were also used in wound segmentation problems [13; 14]. In wound image analysis, following wound segmentation comes wound classification to analyze only the wound area itself. Traditional ML algorithms are widely chosen by authors for medical image classification. One reason for its wide application is the lack of an amount of labeled wound images, which makes DL inappropriate and over-expensive for this case. In recent years, several authors tried to address burn assessment with ML methods [15; 16; 17]. [PERSON] et al. focused on the classification of scalding burns into different categories with support vector machine (SVM) and k-nearest neighbors (KNN) classifiers [16]. Color and texture features were extracted from the LAB color space. SVM classifier gave the best classification results with 85% in first-degree burns, 87.5% in second-degree burns, and 92.5% in third-degree burns. Several research works compared traditional ML algorithms to determine the best model for their specific dataset and problem. Regarding the diagnosis of pressure ulcers, three different ML approaches (artificial neural networks, SVM, and random forest (RF)) were compared to classify each segmented region as a specific tissue type [18]. They extracted color, texture, morphological, and topological characteristics and selected them with a wrapper approach with recursive feature elimination. Moreover, four different classifiers were also evaluated: linear discriminant analysis (LDA), RF, naive Bayes (NB), and decision trees (DT) [19]. A classification system with NB and SVM algorithms to describe granulation, necrotic, and slough tissues was proposed in [20]. A total of 5 color and 10 texture features were extracted from 45 color channels, from which only 50 had statistical significance. SVM is widely regarded as the best algorithm for classification and is considered very appropriate for these types of problems.