Dense Encoder-Decoder–Based Architecture for Skin Lesion Segmentation

Melanoma is one kind of dangerous cancer that has been increasing rapidly in the world. Initial diagnosis is essential to survival, but often the disease is diagnosed in the fatal stage. The rapid growth of skin cancers raises a huge demand for accurate automatic skin lesion segmentation. While deep learning techniques, i.e., convolutional neural network (CNN), have been widely used for precise segmentation, the existing densely connected network (DenseNet) and residual network (ResNet)–based encoder-decoder architectures used non-biomedical features for skin lesion tasks. The complexity of tuned parameters, small information in the pre-trained features, and the lack of multi-scale information degrade the performance of skin lesion segmentation. To address these issues, we present encoder-decoder–based CNN for skin lesion segmentation, based on the widely used UNet architecture. We exploit the benefit of combining DenseNet and ResNet to improve the performance of skin lesion segmentation. In the encoder path, atrous spatial pyramid pooling (ASPP) is used to generate multi-scale features from different dilation rates. We used dense skip connection to combine the feature maps of both encoder and decoder paths. We evaluate our approach on ISIC 2018 dataset and achieve competitive performance as compared to other state-of-the-art approaches. Compared to the previous UNet approaches, our method gains a high Jaccard index, Dice, accuracy, and sensitivity. We think that this progress is mainly due to the combined architecture of DenseNet, ResNet, ASPP, and dense skip connection that preserve the contextual information in the encoder-decoder paths. We utilized the combined benefits of both recent DenseNet and ResNet architectures. We used ASPP to exploit multi-scale contextual information by adopting multiple dilation rates. We also implemented dense skip connections for better recovery of fine-grained information of target objects. In the future, we believe that this approach will be helpful to other medical image segmentation tasks.