# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
###### Abstract  
Skin lesion segmentation has a critical role in the early and accurate diagnosis of skin cancer by computerized systems. However, automatic segmentation of skin lesions in dermoscopic images is a challenging task owing to difficulties including artifacts (hairs, gel bubbles, ruler markers), indistinct boundaries, low contrast and varying sizes and shapes of the lesion images. This paper proposes a novel and effective pipeline for skin lesion segmentation in dermoscopic images combining a deep convolutional neural network named as You Only Look Once (YOLO) and the GrabCut algorithm. This method performs lesion segmentation using a dermoscopic image in four steps: 1. Removal of hairs on the lesion, 2. Detection of the lesion location, 3. Segmentation of the lesion area from the background, 4. Post-processing with morphological operators. The method was evaluated on two publicly well-known datasets, that is the PH2 and the ISBI 2017 (Skin Lesion Analysis Towards Melanoma Detection Challenge Dataset). The proposed pipeline model has achieved a 90% sensitivity rate on the ISBI 2017 dataset, outperforming other deep learning-based methods. The method also obtained close results according to the results obtained from other methods in the literature in terms of metrics of accuracy, specificity, Dice coefficient, and Jaccard index. skin cancer; skin lesion segmentation; melanoma; convolutional neural networks; Yolo; GrabCut +
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observer [8; 9]. However, the use of human vision alone for the detection of melanoma in dermoscopic images may be inaccurate, subjective, or irreproducible because it depends on the dermatologist's experience [10]. Diagnostic accuracy of melanoma from the dermoscopic images by an inexperienced specialist is between 75% to 84% [8]. In order to overcome all these difficulties encountered in the diagnosis of melanoma, computer-aided diagnosis (CAD) systems are needed to assist the experts in the diagnosis process. There are four steps in CAD systems for identifying a lesion as melanoma: preprocessing, segmentation, feature extraction, and classification. For a robust identification of melanoma, lesion segmentation is a fundamental step in CAD systems. However, this segmentation step is a troublesome process due to the large differences in color, texture, position, and size of skin lesions in dermoscopic images. Besides, the low contrast of the image prevents the differentiation of the adjacent tissues. In addition, extra factors such as air bubbles, hair, ebony frames, ruler marks, blood vessels, and color illumination cause extra difficulties to the lesion segmentation. Figure 1 shows several dermoscopic image samples from the data set with different artifacts on them. Various methods have been proposed for the segmentation of skin lesions. Particularly in recent years, convolutional neural networks (CNNs), which is one of the deep learning methods, has achieved very successful results in segmentation of skin lesions [11]. However, CNNs accepts low- resolution images for decreasing the number of calculations and parameters in the network [11]. This situation may lead to the loss of some important features in the image. The motivation of this paper is to develop a resolution independent method for skin lesion segmentation in dermoscopic images. For this purpose, a pipeline was proposed combining the GrabCut algorithm [12] and the power of deep learning. The suggested pipeline consists of four steps. The first step is removing hairs on the lesion; the second step is detecting the location of the lesion in the image and drawing a bounding box around the skin lesion; the third step is using the bounding box in the GrabCut algorithm for lesion segmentation; and the last step is removing artifacts in the segmented area using morphological image processing operators. To the best of our knowledge, there is no study using deep convolutional neural networks and the GrabCut algorithm together for lesion segmentation.