  In the last few years, Deep Learning (DL) has been showing superior
performance in different modalities of biomedical image analysis. Several DL
architectures have been proposed for classification, segmentation, and
detection tasks in medical imaging and computational pathology. In this paper,
we propose a new DL architecture, the NABLA-N network, with better feature
fusion techniques in decoding units for dermoscopic image segmentation tasks.
The NABLA-N network has several advances for segmentation tasks. First, this
model ensures better feature representation for semantic segmentation with a
combination of low to high-level feature maps. Second, this network shows
better quantitative and qualitative results with the same or fewer network
parameters compared to other methods. In addition, the Inception Recurrent
Residual Convolutional Neural Network (IRRCNN) model is used for skin cancer
classification. The proposed NABLA-N network and IRRCNN models are evaluated
for skin cancer segmentation and classification on the benchmark datasets from
the International Skin Imaging Collaboration 2018 (ISIC-2018). The experimental
results show superior performance on segmentation tasks compared to the
Recurrent Residual U-Net (R2U-Net). The classification model shows around 87%
testing accuracy for dermoscopic skin cancer classification on ISIC2018
dataset.
