Classification of skin pigmented lesions based on deep residual network

Yunfei Qi*, Shaofu Lin, Zhisheng Huang

*Corresponding author for this work

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


There are various of skin pigmented lesions with high risk. Melanoma is one of the most dangerous forms of skin cancer. It is one of the important research directions of medical artificial intelligence to carry out classification research of skin pigmented lesions based on deep learning. It can assist doctors to make clinical diagnosis and make patients receive treatment as soon as possible to improve survival rate. Aiming at the similar and imbalanced dermoscopic image data of pigmented lesions, this paper proposes a deep residual network improved by Squeeze-and-Excitation module, and dynamic update class-weight, in batches, with model ensemble adjustment strategies to change the attention of imbalanced data. The results show that the above method can increase the average precision by 9.1%, the average recall by 15.3%, and the average F1-score by 12.2%, compared with the multi-class classification using the deep residual network. Thus, the above method is a better classification model and weight adjustment strategy.

Original languageEnglish
Title of host publicationHealth Information Science
Subtitle of host publication8th International Conference, HIS 2019, Xi'an, China, October 18–20, 2019, Proceedings
EditorsHua Wang, Siuly Siuly, Yanchun Zhang, Rui Zhou, Fernando Martin-Sanchez, Zhisheng Huang
Number of pages10
ISBN (Electronic)9783030329624
ISBN (Print)9783030329617
Publication statusPublished - 2019
Event8th International Conference on Health Information Science, HIS 2019 - Xi'an, China
Duration: 18 Oct 201920 Oct 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11837 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference8th International Conference on Health Information Science, HIS 2019


  • Deep learning
  • Imbalanced data
  • Model ensemble
  • Multi-classification
  • Residual network
  • Skin lesions


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