Deep-learning models for image-based gynecological cancer diagnosis: a systematic review and meta- analysis.

Journal: Frontiers in oncology
Published Date: Jan 11, 2024

Abstract

INTRODUCTION: Gynecological cancers pose a significant threat to women worldwide, especially those in resource-limited settings. Human analysis of images remains the primary method of diagnosis, but it can be inconsistent and inaccurate. Deep learning (DL) can potentially enhance image-based diagnosis by providing objective and accurate results. This systematic review and meta-analysis aimed to summarize the recent advances of deep learning (DL) techniques for gynecological cancer diagnosis using various images and explore their future implications.

Authors

  • Asefa Adimasu Taddese
    Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Binyam Chakilu Tilahun
    Department of Health Informatics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Tadesse Awoke
    Department of Epidemiology and Biostatistics, Institute of Public Health, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
  • Asmamaw Atnafu
    eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia.
  • Adane Mamuye
    eHealthlab Ethiopia Research Center, University of Gondar, Gondar, Ethiopia.
  • Shegaw Anagaw Mengiste
    Department of Business, History and Social Sciences, University of Southeastern Norway, Vestfold, Vestfold, Norway.

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