Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features

Zhen Yu, Xudong Jiang, Feng Zhou, Jing Qin, Dong Ni, Siping Chen, Baiying Lei, Tianfu Wang

Research output: Journal article publicationJournal articleAcademic researchpeer-review

192 Citations (Scopus)


In this paper, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a support vector machine with a Chi-squared kernel. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. Comparisons with state-of-the-art methods show the superiority of our method using the publicly available ISBI 2016 Skin lesion challenge dataset.

Original languageEnglish
Article number8440053
Pages (from-to)1006-1016
Number of pages11
JournalIEEE Transactions on Biomedical Engineering
Issue number4
Publication statusPublished - Apr 2019


  • deep learning
  • Dermoscopy image
  • fisher vector
  • melanoma recognition
  • residual network

ASJC Scopus subject areas

  • Biomedical Engineering


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