# Skin Lesion Segmentation in Dermoscopic Images with Combination of YOLO and GrabCut Algorithm
## 2 Related Works
Evaluated on two publicly well-known datasets, the ISBI 2016 and the PH2, it achieved an accuracy of 95.5% and 93.7% respectively. [PERSON] et al. developed a multistage FCN and a parallel integration (PI) method to segment skin lesions in dermoscopic images [10]. The PI method combined with the FCN helps further improve the boundaries of the segmented skin lesions. It was evaluated on two publicly available datasets, the ISBI 2016 and the PH2 [52], attaining 95.51% and 94.24% of accuracy rates, 91.18% and 90.66% of Dice coefficient indices respectively. In another study in 2017, [PERSON] et al. introduced a deep network for multi-class semantic skin lesion segmentation by means of FCN. Their deep FCN architecture succeeded in segmentation of three class of skin lesions including melanoma, benign nevi, and seborrheic keratoses. They used the ISBI 2017 Challenge dataset for evaluation of the method. This deep FCN architecture attained Dice coefficient indices of 55.7%, 65.3%, and 78.5%, for and seborrheic keratosis, melanoma, and benign lesions, respectively [53]. [PERSON] et al. compared the performance of two skin lesion segmentation approaches. One of them is deep convolutional neural network-based U-Net and another one is C-Means Clustering method [54]. This comparison tested on the ISBI 2017 Challenge dataset [50]. The U-Net achieved 77% Dice coefficient indices while clustering method remained at 61%. The results show that U-net method significantly outperformed the clustering method. [PERSON] et al. presented a deep neural network architecture consisting of convolutional and deconvolutional layers (CDNN) for skin lesion segmentation in 2017 [55]. They trained their model with the ISBI 2017 dataset using dermoscopic images different color spaces. Their CDNN architecture took first place in the ISBI 2017 Challenge with the 76.5% Jaccard index. In 2018, [PERSON] et al. proposed a novel skin lesion segmentation approach called a full resolution convolutional network (FrCN) [56]. The advantage of this model is to eliminate subsampling layers and use full resolution input in the architecture during the training. In this way, the desired specific features could be obtained from the input image easily. They evaluated their deep model on two publicly available datasets, the PH2 and the ISBI 2017. Test results for sensitivity, specificity, accuracy on the ISBI 2017 dataset were 85.40%, 96.69%, 94.03%, respectively, while on the PH2 dataset, they were 93.72%, 95.65%, 95.08%, respectively. In 2018, [PERSON] et al. presented a deep model called dense deconvolutional network (DNN) for the segmentation of skin lesions. Their model consists of dense deconvolutional layers (DDL), chained residual pooling (CRP) and hierarchical supervision (HS) [57]. They trained DDL for maintaining the same resolutions of input and output images without prior knowledge or complicated post-processing procedures. They used CRP for extracting rich contextual information by combining local and global contextual feature combination and used HS to serve as a loss helper as well as to improve the prediction mask. They used the ISBI 2017 dataset for evaluation of their model and obtained the results 0.866%, 0.765%, 0.939%, for Dice coefficient, Jaccard index, accuracy, respectively. In 2018, [PERSON] et al. used a segmentation architecture based on adversarial networks. They utilized generative adversarial network (GAN) to assist segmentation of skin lesions [58]. They used a U-net based network as generator and a CNN network as discriminator to discriminate the ground truth and generated mask. They evaluated their model on the ISBI 2016 dataset and achieved an average segmentation accuracy rate of 0.97% and a 0.94% the Dice coefficient rate. Recently, in 2019, [PERSON] et al. recommended a segmentation method [59], an improved version of their last study [51]. They developed a deeper network architecture with 29 layers and used small kernel filters for attaining more detailed features and increasing the discrimination capacity of their architecture.