  According to WHO[1], since the 1970s, diagnosis of melanoma skin cancer has
been more frequent. However, if detected early, the 5-year survival rate for
melanoma can increase to 99 percent. In this regard, skin lesion segmentation
can be pivotal in monitoring and treatment planning. In this work, ten models
and four augmentation configurations are trained on the ISIC 2016 dataset. The
performance and overfitting are compared utilizing five metrics. Our results
show that the U-Net-Resnet50 and the R2U-Net have the highest metrics value,
along with two data augmentation scenarios. We also investigate CBAM and AG
blocks in the U-Net architecture, which enhances segmentation performance at a
meager computational cost. In addition, we propose using pyramid, AG, and CBAM
blocks in a sequence, which significantly surpasses the results of using the
two individually. Finally, our experiments show that models that have exploited
attention modules successfully overcome common skin lesion segmentation
problems. Lastly, in the spirit of reproducible research, we implement models
and codes publicly available.
