Through this study, we present a new and automatic semantic segmentation network for robust skin lesion segmentation named Dermoscopic Skin Network (DSNet). In order to reduce the number of parameters to make the network lightweight, we used a depth-wise separable convolution in lieu of standard convolution to project the learnt discriminating features onto the pixel space at different stages of the encoder.
The directory of the repository is given below-
We evaluate our proposed model on two publicly available datasets, namely ISIC-2017 and PH2. The obtained mean Intersection over Union (mIoU) is 77.5 % and 87.0 % respectively for ISIC-2017 and PH2 datasets which outperformed the ISIC-2017 challenge winner by 1.0 % with respect to mIoU. Our proposed network also outperformed U-Net and FCN8s respectively by 3.6 % and 6.8 % with respect to mIoU on the ISIC-2017 dataset.
The more details of the proposed DSNet for the automatic and robust skin lesion segmentation can be found in the paper given below-
https://www.sciencedirect.com/science/article/abs/pii/S0010482520301190
Md. Kamrul Hasan
Erasmus Scholar on Medical Imaging and Application (MAIA) [http://maiamaster.udg.edu/]
For more details write me at kamruleeekuet@gmail.com