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

authored by: 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.
Organisation(s): Hannover Centre for Optical Technologies (HOT)
PhoenixD: Photonics, Optics, and Engineering - Innovation Across Disciplines
Type: Article
Journal: Journal of biomedical optics
Volume: 29
No. of pages: 1
ISSN: 1083-3668
Publication date: 19.11.2024
Publication status: Published
Peer reviewed: Yes
ASJC Scopus subject areas: Electronic, Optical and Magnetic Materials, Biomaterials, Atomic and Molecular Physics, and Optics, Biomedical Engineering
Sustainable Development Goals: SDG 3 - Good Health and Well-being
Electronic version(s): https://doi.org/10.1117/1.JBO.29.11.116003 (Access: Open)