## 2 Related Work  
In different computer vision tasks, the convolutional neural network models have made exceptional progress [28; 29; 30; 31; 32], and have been proven to be the best in solving segmentation problems. Skin lesion classification accuracy directly depends on the lesion segmentation. The more accurate the lesion segmentation is, the better the result of lesion classification. Lately, a lot of research has been done to develop such models, which can automatically conduct the lesion segmentation in such a way that differentiates the normal area of skin from the lesion. Different techniques have been used, such as region growing, threshold, edge detection, etc. to develop such models. Segmentation problems have been treated as classification problems by some researchers, such as [PERSON] et al. [33]. They proposed an automatic skin segmentation model for non-demoscopic images. For better performance, this model uses the guided filter technique [34] as a pre-processing technique to reduce the noise artefacts on the input images. A window around the pixel is placed to identify whether it belongs to a lesion area or not and then is fed to a CNN and output labels pixel in the centre of the patch. A segmentation map is generated with the label as 1 (lesion) and 0 (normal) for each pixel of the pre-processed images and then the segmentation mask is generated by selecting the largest connected component of an image. The output of this model is the predicted labels of the pixels. Nonetheless, such a model requires a dense prediction because it is based on pixel-level prediction. U-NET is a well-known model proposed by [PERSON] et al. [20]. It has a good success rate for medical images when it comes to segmentation problems. Due to its popularity, many models have been proposed [21; 22; 23] adopting U-NET architecture. They have used it for melanoma skin segmentation and classification. In [23], the authors have modified the original U-NET by widening the kernel at each convolutional block using a dilated convolution technique in order to expand the receptive field of the proposed model; whereas in [24], the authors have utilized colour transformation to select different colour bands in order to improve the performance of the proposed approach. Another model proposed by [PERSON] et al. [35] incorporated the deconvolution method and replaced the regular cross-entropy function with a loss function based on Jaccard distance; whereas a full resolution CNN model is proposed by [PERSON] et al. [36], in which models directly learn the full resolution features of every single pixel from the input images without reducing the size of input images. However, there is an issue of using full resolution CNN as an overfitting of the model, which is likely to happen on the features of non-melanoma skin lesions. In that case, such models perform poorly when it comes to complex features of melanoma skin lesions due to their inconsistent boundary appearance and different textures. [PERSON] et al. [37] proposed a new model to overcome the limitation of full-resolution CNN. Their model learns the necessary skin lesion characteristics for each class (be it melanoma or non-melanoma) individually. They incorporated a new probability-based, stepwise integration to combine the segmentation output generated from each class. In another study [38] of skin lesion segmentation, a dilated residual network has been used along with pyramid pooling networks. To attain sharp boundaries, they have used a negative log-likelihood and endpoint error loss together. In a recent study, a bootstrapping CNN method was proposed by [PERSON] et al. [17] for both skin lesion segmentation and skin classification problem. In this method, one task aids another in a bootstrapping way where a coarse segmentation network is trained, and then a predicted coarse mask is utilized to guide the classification network. Simultaneously, localization maps are generated to increase the prediction of masks outperforming the coarse mask.