Deep learning algorithms for melanoma detection using dermoscopic images: A systematic review and meta-analysis.

Journal: Artificial intelligence in medicine
Published Date: Jul 25, 2024

Abstract

BACKGROUND: Melanoma is a serious risk to human health and early identification is vital for treatment success. Deep learning (DL) has the potential to detect cancer using imaging technologies and many studies provide evidence that DL algorithms can achieve high accuracy in melanoma diagnostics.

Authors

  • Zichen Ye
    Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Daqian Zhang
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yuankai Zhao
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Mingyang Chen
    School of Manufacturing Science and Engineering, Key Laboratory of Testing Technology for Manufacturing Process, Ministry of Education, Southwest University of Science and Technology, Mianyang, China.
  • Huike Wang
    School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Samuel Seery
    Faculty of Health and Medicine, Division of Health Research, Lancaster University, Lancaster, United Kingdom.
  • Yimin Qu
    Department of Epidemiology and Biostatistics, School of Population Medicine and Public Health, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Peng Xue
    National Cancer Center/National Clinical Research Center for Cancer, Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
  • Yu Jiang
    School of Pharmacy and Food Engineering, Wuyi University, Jiangmen, China.

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