Skin Lesion Segmentation Based on Improved U-net

Lina Liu, Lichao Mou, Xiao Xiang Zhu, Mrinal Mandal

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

45 Scopus citations

Abstract

Melanoma is one of the most common and dangerous skin cancers, accounting for 75% of deaths associated with skin cancer. Detection of melanoma in early stages can significantly improve the survival rate. Automatic segmentation of melanoma is an important and essential step for accurate detection of melanoma. Many existing works based on traditional segmentation methods and deep learning methods have been proposed for high-resolution dermoscopy images. However, due to the intrinsic visual complexity and ambiguity among different skin conditions, automatic melanoma segmentation is still a challenging task for existing methods. Among these methods, the deep learning methods have obtained more attention recently due to its high performance by training an end-to-end framework, which needs no human interaction. U-net is a very popular deep learning model for medical image segmentation. In this paper, we propose an efficient skin lesion segmentation based on improved U-net model. Experiments conducted on the 2017 ISIC Challenge dataset towards melanoma detection shows that the proposed method can obtain state-of-the-art performance on skin lesion segmentation task.

Original languageEnglish
Title of host publication2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728103198
DOIs
StatePublished - May 2019
Event2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019 - Edmonton, Canada
Duration: 5 May 20198 May 2019

Publication series

Name2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019

Conference

Conference2019 IEEE Canadian Conference of Electrical and Computer Engineering, CCECE 2019
Country/TerritoryCanada
CityEdmonton
Period5/05/198/05/19

Keywords

  • Dilated Convolution
  • Skin Lesion Segmentation
  • U-net

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