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

verfasst von

Lennart Jütte, Harshkumar Patel, Bernhard Roth

Abstract

Significance: Early detection of melanoma is crucial for improving patient outcomes, and dermoscopy is a critical tool for this purpose. However, hair presence in dermoscopic images can obscure important features, complicating the diagnostic process. Enhancing image clarity by removing hair without compromising lesion integrity can significantly aid dermatologists in accurate melanoma detection. Aim: We aim to develop a novel synthetic hair dermoscopic image dataset and a deep learning model specifically designed for hair removal in melanoma dermoscopy images. Approach: To address the challenge of hair in dermoscopic images, we created a comprehensive synthetic hair dataset that simulates various hair types and dimensions over melanoma lesions. We then designed a convolutional neural network (CNN)-based model that focuses on effective hair removal while preserving the integrity of the melanoma lesions. Results: The CNN-based model demonstrated significant improvements in the clarity and diagnostic utility of dermoscopic images. The enhanced images provided by our model offer a valuable tool for the dermatological community, aiding in more accurate and efficient melanoma detection. Conclusions: 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.

Organisationseinheit(en)

Hannoversches Zentrum für Optische Technologien (HOT)
PhoenixD: Simulation, Fabrikation und Anwendung optischer Systeme

Typ

Artikel

Journal

Journal of biomedical optics

Band

29

Anzahl der Seiten

1

ISSN

1083-3668

Publikationsdatum

19.11.2024

Publikationsstatus

Veröffentlicht

Peer-reviewed

Ja

ASJC Scopus Sachgebiete

Elektronische, optische und magnetische Materialien, Biomaterialien, Atom- und Molekularphysik sowie Optik, Biomedizintechnik

Ziele für nachhaltige Entwicklung

SDG 3 – Gute Gesundheit und Wohlergehen

Elektronische Version(en)

https://doi.org/10.1117/1.JBO.29.11.116003 (Zugang: Offen)