A Study of Skin Reflectance Using Kubelka-Munk Model

Cho, A Ra; Kim, Su Ji; Lee, Jun Bae; Sim, Geon Young; Back, Min; Cho, Eun Seul; Jang, Ji Hui; Jang, Eunseon; Kim, Youn Joon; Yoo, Kweon Jong; Han, Jeong Woo

doi:10.15230/scsk.2016.42.1.45

Cited by 2 publications

(2 citation statements)

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“…In the medical field, HSI can be used to investigate tissue structures and diagnose lesions 16–18 . In particular, it obtains absorption and reflection information of the skin layer, as the penetration depth of light into the skin varies with respect to wavelength 19–21 . Consequently, HSI non‐invasively enables the visual confirmation of structural features, which holds potential for clinical applications 22 .…”

Section: Introductionmentioning

confidence: 99%

“… 16 , 17 , 18 In particular, it obtains absorption and reflection information of the skin layer, as the penetration depth of light into the skin varies with respect to wavelength. 19 , 20 , 21 Consequently, HSI non‐invasively enables the visual confirmation of structural features, which holds potential for clinical applications. 22 Using this information, it is possible to assess the sensitivity of lesion tissues.…”

Section: Introductionmentioning

confidence: 99%

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Parameter‐based transfer learning for severity classification of atopic dermatitis using hyperspectral imaging

Kim,

Baek,

Lee

2024

Skin Research and Technology

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Background/purposeBecause atopic dermatitis (AD) is a chronic inflammatory skin condition that causes structural changes, there is a growing need for noninvasive research methods to evaluate this condition. Hyperspectral imaging (HSI) captures skin structure features by exploiting light wavelength variations in penetration depth. In this study, parameter‐based transfer learning was deployed to classify the severity of AD using HSI. Therefore, we aimed to obtain an optimal combination of classification results from the four models after constructing different source‐ and target‐domain datasets.MethodsWe designated psoriasis, skin cancer, eczema, and AD datasets as the source datasets, and the set of images acquired via hyperspectral camera as the target dataset for wavelength‐specific AD classification. We compared the severity classification performances of 96 combinations of sources, models, and targets.ResultsThe highest classification performance of 83% was achieved when ResNet50 was trained on the augmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target Near‐infrared radiation (NIR) dataset. The second highest classification accuracy of 81% was achieved when ResNet50 was trained on the unaugmented psoriasis dataset as the source, with the resulting parameters used to train the model on the target R dataset. ResNet50 demonstrated potential as a generalized model for both the source and target data, also confirming that the psoriasis dataset is an effective training resource.ConclusionThe present study not only demonstrates the feasibility of the severity classification of AD based on hyperspectral images, but also showcases combinations and research scalability for domain exploration.

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Section: Introductionmentioning

confidence: 99%