{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/190411126","title":"Skin Cancer Segmentation and Classification with NABLA-N and Inception Recurrent Residual Convolutional Networks","arxiv_id":"1904.11126","date":"2019-04-25","proceeding":null,"authors":["Md Zahangir Alom","Theus Aspiras","Tarek M. Taha","Vijayan K. Asari"],"abstract":"In the last few years, Deep Learning (DL) has been showing superior\nperformance in different modalities of biomedical image analysis. Several DL\narchitectures have been proposed for classification, segmentation, and\ndetection tasks in medical imaging and computational pathology. In this paper,\nwe propose a new DL architecture, the NABLA-N network, with better feature\nfusion techniques in decoding units for dermoscopic image segmentation tasks.\nThe NABLA-N network has several advances for segmentation tasks. First, this\nmodel ensures better feature representation for semantic segmentation with a\ncombination of low to high-level feature maps. Second, this network shows\nbetter quantitative and qualitative results with the same or fewer network\nparameters compared to other methods. In addition, the Inception Recurrent\nResidual Convolutional Neural Network (IRRCNN) model is used for skin cancer\nclassification. The proposed NABLA-N network and IRRCNN models are evaluated\nfor skin cancer segmentation and classification on the benchmark datasets from\nthe International Skin Imaging Collaboration 2018 (ISIC-2018). The experimental\nresults show superior performance on segmentation tasks compared to the\nRecurrent Residual U-Net (R2U-Net). The classification model shows around 87%\ntesting accuracy for dermoscopic skin cancer classification on ISIC2018\ndataset.","url_abs":"http://arxiv.org/abs/1904.11126v1","url_pdf":"http://arxiv.org/pdf/1904.11126v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"190411126","repo_url":"https://github.com/CristianLazoQuispe/skin-lesion-segmentation-using-pix2pix","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"cancer-classification","task_name":"Cancer Classification"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"segmentation","task_name":"Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"skin-cancer-classification","task_name":"Skin Cancer Classification"},{"task_slug":"skin-cancer-segmentation","task_name":"Skin Cancer Segmentation"}],"methods":[{"method_slug":"concatenated-skip-connection","method_name":"Concatenated Skip Connection"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"max-pooling","method_name":"Max Pooling"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"u-net","method_name":"U-Net"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}