# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 2 Related Works  
Specific and prominent features of lesion images play a critical role in the classification of melanoma. These features can only be obtained by proper segmentation of the skin lesion from surrounding tissue. Segmenting the lesion from the surrounding normal tissue and extracting more representative features are essential for a robust and effective diagnosis [14; 15]. There are several segmentation methods developed to segment skin lesions automatically or semi-automatically [16; 17; 18; 19]. These segmentation methods can be grouped in to five. Histogram thresholding methods try to identify a threshold value for the segmentation of lesion from the surrounding tissue [20; 21; 22]. Unsupervised clustering approaches use the color space properties of RGB dermoscopic images to obtain homogenous regions [23; 24; 25; 26; 27; 28; 29]. Edge-based and region-based methods take advantage of the edge operator and different algorithms such as region splitting or merging [28; 30; 31]. Active contour methods utilize metaheuristic algorithms, genetic algorithms and snake algorithms, etc., for segmentation of lesion area [24; 32; 33; 34]. The last group is the supervised segmentation methods. These methods segment the skin lesion by training the recognizers, such support vector machines (SVMs), decision trees (DTs), and artificial neural networks (ANNs) [24; 35]. More detailed information on these methods can be found in the most comprehensive and current reviews of segmentation techniques used in skin lesions [17; 36; 37]. All these techniques use low level features that rely on pixel level features. Therefore, these classical segmentation techniques are unable to achieve satisfactory results and cannot overcome difficulties such as fuzzy lesion boundaries, hair artifacts, low contrast, and other artifacts. Nowadays, deep learning-based methods especially CNNs have obtained significant success in image classification, object detection, and segmentation problems [13; 38; 39]. The main reason behind the success of CNNs is their capability of hierarchical feature learning and extracting more high level and robust features from the raw image data. There are different types of CNN architectures for different purposes such as classification, segmentation, object detection and localization [13; 40; 41]. In addition to the natural image classification, CNNs also achieved great success in diversified medical problems such as detection of mitosis in histology images [42], brain tumor segmentation in MR images [43], breast cancer detection in mammography images etc. [44]. A detailed review is presented by [PERSON] et al. [45]. Also, CNNs achieved state-of-the-art results in semantic segmentation. Various deep CNN architectures have been proposed for semantic segmentation such as Fully Convolutional Neural Network (FCN) [44], U-Net [41], SegNet [46], and DeepLab [47]. More about these semantic segmentation methods can be found in a detailed review [48]. New developments in CNN architectures with the capability of semantic segmentation have been used in segmentation of skin lesions by researchers in recent years. For instance, in 2017, [PERSON] et al. presented an end-to-end deep network consisting of two stages called segmentation and classification [49]. They developed a fully convolutional residual network (FCRN) utilizing the power of deep residual networks and the took second place with the accuracy of 94.9% in the segmentation category of the International Symposium on Biomedical Imaging (ISBI) 2016 Challenge [50]. Another type of a deep residual network (DRN) introduced by the same team took first place with the accuracy of 85.5% in the classification category of the same challenge. In another study, [PERSON] et al., introduced a skin lesion segmentation technique by utilizing FCN [51]. They improved the FCN model by using an unusual loss function named as Jaccard distance. In this way, they solved the imbalance problem between the surrounding skin and lesion pixels.