Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal

The introduction of our synthetic hair dermoscopic image dataset and CNN-based model represents a significant advancement in medical image analysis for melanoma detection. By effectively removing hair from dermoscopic images while preserving lesion details, our approach enhances diagnostic accuracy and supports early melanoma detection efforts.

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Publication Details

Journal
Journal of Biomedical Optics
Published
2024-11-19
DOI
https://doi.org/10.1117/1.jbo.29.11.116003
Citations
6
Primary Topic
Cutaneous Melanoma Detection and Management
Type
article
Field-Weighted Citation Impact
1.70
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article

Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal

Bernhard Roth, Harshkumar Patel, Lennart Jütte
6 citations
Journal of Biomedical Optics
Cutaneous Melanoma Detection and Management
1.70
article

Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal

Bernhard Roth, Harshkumar Patel, Lennart Jütte
article en
6 citations

Abstract

The introduction of our synthetic hair dermoscopic image dataset and CNN-based model represents a significant advancement in medical image analysis for melanoma detection. By effectively removing hair from dermoscopic images while preserving lesion details, our approach enhances diagnostic accuracy and supports early melanoma detection efforts.

Journal of Biomedical OpticsVol. 29(11)
Leibniz University Hannover (DE)
Zero hunger
Openalex Percentile: Top 13%
Cutaneous Melanoma Detection and Management
1.70
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