DeepErythema: A Study on the Consistent Evaluation Method of UV SPF Index through Deep Learning

This study presents a deep learning-based approach for consistent Sun Protection Factor (SPF) grading by quantifying skin erythema. Despite strict international standards, the current human-based Minimum Erythema Dose (MED) determination method for SPF evaluation suffers from inconsistency due to su...

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Bibliographic Details
Published in:IEEE access 2023-01, Vol.11, p.1-1
Main Authors: Lee, Cheolwon, Yoo, Sangwook, Lee, Han Na, Lee, Jongha
Format: Article
Language:English
Subjects:
Computer vision
Deep learning
Digital imaging
Erythema
Feature extraction
Image restoration
Image segmentation
Inspection
Irradiation
Lesions
Reduction
Semantic Segmentation
Skin
Specific gravity
Ultraviolet radiation
Ultraviolet sources
UV SPF Index
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Summary:This study presents a deep learning-based approach for consistent Sun Protection Factor (SPF) grading by quantifying skin erythema. Despite strict international standards, the current human-based Minimum Erythema Dose (MED) determination method for SPF evaluation suffers from inconsistency due to subjective criteria and the visual characteristics of erythema's large variance. We propose DeepErythema, a novel method that uses erythema quantification for MED determination. The proposed method comprises pre-processing methods, a deep learning segmentation model, and post-processing methods. The UV irradiation area pointing is a pre-processing method that accurately detects the inspection area as a UV irradiated port, while Median Gradation Reduction eliminates the gradation that arises due to the digital image collection environment. The deep learning segmentation model parts introduce a methodology for improving performance using various feature extractors and methodologies like SeLu, Reverse Attention Gate, and MixedLoss. For post-processing, Perspective Transform Rectangle Restoration restores a distorted inspection area, and Relative Density Evaluation calculates a density score to consider skin characteristics and tone. We verified that utilizing the DeepErythema score in the UV SPF MED Evaluation (USME) dataset reduced the distribution of human-based MED decisions from 36 to 9 in the experimental results. This reduction in MED decision scope leads to improved consistency in SPF Index grading. Overall, the study contributes to the development of an objective and consistent evaluation method for SPF grading.
ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2023.3284892