Evidence map›Paper›PMID 41941017›Full record

ReviewJournal of imaging informatics in medicine2026

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

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

Abstract readReview
PubMed Publisher
In one paragraph

Review in Journal of imaging informatics in medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Dalal BardouLMIA Lab, Department of Computer Science, Abbes Laghrour University of Khenchela, 40000, Khenchela, Algeria. dalal.bardou@univ-khenchela.dz.ORCID http://orcid.org/0000-0002-9169-863X
Hamida BouazizMécatronique Laboratory, Department of Computer Science, Jijel University, 18000, Jijel, Algeria.
Laishui LvCollege of Information Engineering, China Jiliang University Hangzhou, 310018, Hangzhou, China.
Mourad BounezraIndependent Researcher, 40000, Khenchela, Algeria.
Ahmadreza VajdiSchool of Computer Science and Engineering, Nanjing University of Science and Technology, 210094, Nanjing, China.
Ting ZhangCollege of Computer and Electronic Information, Nanjing Normal University, Nanjing, 210098, China. zting135@nnu.edu.cn.
Fayçal AbbasLESIA Laboratory, Computer Science Department, University of Abbes Laghrour, 40000, Khenchela, Algeria.
Mehdi MalahComputer Science Department, Mohamed Cherif Messaadia University-Souk Ahras, 41000, Souk Ahras, Algeria.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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.

Indexed as

Conventional techniquesDeep learningGap inpaintingHair removalHair segmentationMachine learning

Identifiers

PMID41941017

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.