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<p>Early melanoma detection is made possible by dermoscopy, but automated analysis and clinical evaluation are often hampered by hair artifacts in images. Although hair removal is critical for accurate diagnosis, it has frequently been treated as a minor preprocessing step rather than a separate research focus. This review provides the first thorough investigation into automatic hair removal in dermoscopy images. We categorize and evaluate existing techniques, from conventional image processing to more recent deep learning (DL) architectures, such as generative models and hybrid approaches. This review covers literature published from 1990 to 2025, highlighting the advancements made over the past three decades and the current state of research. Finally, future research directions and challenges are discussed.</p>

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A Review of Automatic Hair Removal in Dermoscopy Images: From Image Processing to Deep Learning

  • 期刊论文

  • 发表日期：

    2026年4月6日

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  • Dalal Bardou,
  • Hamida Bouaziz,
  • Laishui Lv,
  • Mourad Bounezra,
  • Ahmadreza Vajdi,
  • Ting Zhang,
  • Fayçal Abbas,
  • Mehdi Malah

摘要

Early melanoma detection is made possible by dermoscopy, but automated analysis and clinical evaluation are often hampered by hair artifacts in images. Although hair removal is critical for accurate diagnosis, it has frequently been treated as a minor preprocessing step rather than a separate research focus. This review provides the first thorough investigation into automatic hair removal in dermoscopy images. We categorize and evaluate existing techniques, from conventional image processing to more recent deep learning (DL) architectures, such as generative models and hybrid approaches. This review covers literature published from 1990 to 2025, highlighting the advancements made over the past three decades and the current state of research. Finally, future research directions and challenges are discussed.