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In the last few years, Deep Learning (DL) has been showing superiorperformance in different modalities of biomedical image analysis. Several DLarchitectures have been proposed for classification, segmentation, anddetection tasks in medical imaging and computational pathology. In this paper,we propose a new DL architecture, the NABLA-N network, with better featurefusion techniques in decoding units for dermoscopic image segmentation tasks.The NABLA-N network has several advances for segmentation tasks. First, thismodel ensures better feature representation for semantic segmentation with acombination of low to high-level feature maps. Second, this network showsbetter quantitative and qualitative results with the same or fewer networkparameters compared to other methods. In addition, the Inception RecurrentResidual Convolutional Neural Network (IRRCNN) model is used for skin cancerclassification. The proposed NABLA-N network and IRRCNN models are evaluatedfor skin cancer segmentation and classification on the benchmark datasets fromthe International Skin Imaging Collaboration 2018 (ISIC-2018). The experimentalresults show superior performance on segmentation tasks compared to theRecurrent Residual U-Net (R2U-Net). The classification model shows around 87%testing accuracy for dermoscopic skin cancer classification on ISIC2018dataset.

Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks