Details
| Original language | English |
|---|---|
| Article number | 116003 |
| Number of pages | 1 |
| Journal | Journal of biomedical optics |
| Volume | 29 |
| Issue number | 11 |
| Publication status | Published - 19 Nov 2024 |
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.
Keywords
- deep learning, dermoscopy, melanoma, skin cancer, synthetic hair dataset
ASJC Scopus subject areas
- Materials Science(all)
- Electronic, Optical and Magnetic Materials
- Materials Science(all)
- Biomaterials
- Physics and Astronomy(all)
- Atomic and Molecular Physics, and Optics
- Engineering(all)
- Biomedical Engineering
Sustainable Development Goals
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In: Journal of biomedical optics, Vol. 29, No. 11, 116003, 19.11.2024.
Research output: Contribution to journal › Article › Research › peer review
}
TY - JOUR
T1 - Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal
AU - Jütte, Lennart
AU - Patel, Harshkumar
AU - Roth, Bernhard
N1 - Publisher Copyright: © 2024 The Authors.
PY - 2024/11/19
Y1 - 2024/11/19
N2 - 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.
AB - 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.
KW - deep learning
KW - dermoscopy
KW - melanoma
KW - skin cancer
KW - synthetic hair dataset
UR - http://www.scopus.com/inward/record.url?scp=85210112437&partnerID=8YFLogxK
U2 - 10.1117/1.JBO.29.11.116003
DO - 10.1117/1.JBO.29.11.116003
M3 - Article
C2 - 39564076
AN - SCOPUS:85210112437
VL - 29
JO - Journal of biomedical optics
JF - Journal of biomedical optics
SN - 1083-3668
IS - 11
M1 - 116003
ER -