Please use this identifier to cite or link to this item: http://hdl.handle.net/1893/30446
Appears in Collections:Computing Science and Mathematics Conference Papers and Proceedings
Author(s): Ali, Abder-Rahman
Li, Jingpeng
O'Shea, Sally Jane
Yang, Guang
Trappenberg, Thomas
Ye, Xujiong
Title: A Deep Learning Based Approach to Skin Lesion Border Extraction with a Novel Edge Detector in Dermoscopy Images
Citation: Ali A, Li J, O'Shea SJ, Yang G, Trappenberg T & Ye X (2019) A Deep Learning Based Approach to Skin Lesion Border Extraction with a Novel Edge Detector in Dermoscopy Images. In: Proceedings of the 2019 International Joint Conference on Neural Networks, IJCNN 2019. International Joint Conference on Neural Networks (IJCNN 2019), Budapest, Hungary, 14.07.2019-19.07.2019. Piscataway, NJ, USA: Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/IJCNN.2019.8852134
Issue Date: 30-Sep-2019
Conference Name: International Joint Conference on Neural Networks (IJCNN 2019)
Conference Dates: 2019-07-14 - 2019-07-19
Conference Location: Budapest, Hungary
Abstract: Lesion border detection is considered a crucial step in diagnosing skin cancer. However, performing such a task automatically is challenging due to the low contrast between the surrounding skin and lesion, ambiguous lesion borders, and the presence of artifacts such as hair. In this paper we propose a two-stage approach for skin lesion border detection: (i) segmenting the skin lesion dermoscopy image using U-Net, and (ii) extracting the edges from the segmented image using a novel approach we call FuzzEdge. The proposed approach is compared with another published skin lesion border detection approach, and the results show that our approach performs better in detecting the main borders of the lesion and is more robust to artifacts that might be present in the image. The approach is also compared with the manual border drawings of a dermatologist, resulting in an average Dice similarity of 87.7%.
Status: AM - Accepted Manuscript
Rights: © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

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