A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging
Atopic dermatitis (AD) is a chronic skin disease typically evaluated using the Invesrigator’s Global Assessment (IGA), SCORing Aoptic Dermatitis (SCORAD), and Eczema Area and Severity Index (EASI) (SCORAD and EASI; S/E). However, the subjectivity of the diagnostic process remains limited....
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2025-01-01
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| author | Eun Bin Kim Yoo Sang Baek Onseok Lee |
| author_facet | Eun Bin Kim Yoo Sang Baek Onseok Lee |
| author_sort | Eun Bin Kim |
| collection | DOAJ |
| description | Atopic dermatitis (AD) is a chronic skin disease typically evaluated using the Invesrigator’s Global Assessment (IGA), SCORing Aoptic Dermatitis (SCORAD), and Eczema Area and Severity Index (EASI) (SCORAD and EASI; S/E). However, the subjectivity of the diagnostic process remains limited. Hyperspectral cameras can detect optical signals to simultaneously generate morphological information and collect spatial information. The depth of skin penetration of caries can be determined according to the wavelength of light and its structural feature. Thus, after dividing the colors and information into six datasets, classified and analyzed their features and severities using representative diagnostic evaluation tools. First, the expansion of blood vessels and structural changes in the lesion area were confirmed between 550 and 650 nm, and information analysis of the lesion features was confirmed through the difference in the rate of change between the visible and near-infrared (NIR) areas. Subsequently, severity classification of the datasets for each wavelength of AD was performed. IGA-NIR-DenseNet121 showed the best classification performance, with 88% accuracy and S/E-G-ResNet18 showed 97% accuracy. Thus, IGA is sensitive to NIR, whereas SCORAD and EASI are sensitive to color. Although the overall accuracy was lower than existing RGB images, the t-distributed stochastic neighbor embedding results showed that the severity groups were well distinguished. The newly constructed dataset and severity classification study by wavelength suggested generalizations and methods for diagnosing and evaluating AD. Additionally, it shows the possibility of expanding research by identifying features that appear in the deep skin layers. |
| format | Article |
| id | doaj-art-1e4a7f8693ad4ccb8b7da31e91e0f4fd |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| spelling | doaj-art-1e4a7f8693ad4ccb8b7da31e91e0f4fd2025-01-21T00:01:35ZengIEEEIEEE Access2169-35362025-01-01139209922210.1109/ACCESS.2024.348255710720754A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral ImagingEun Bin Kim0Yoo Sang Baek1https://orcid.org/0000-0002-8667-2814Onseok Lee2https://orcid.org/0000-0002-3696-9353Department of Software Convergence, Graduate School, Soonchunhyang University, Asan, Chungcheongnam-do, Republic of KoreaDepartment of Dermatology, Korea University College of Medicine, Seoul, Republic of KoreaDepartment of Software Convergence, Graduate School, Soonchunhyang University, Asan, Chungcheongnam-do, Republic of KoreaAtopic dermatitis (AD) is a chronic skin disease typically evaluated using the Invesrigator’s Global Assessment (IGA), SCORing Aoptic Dermatitis (SCORAD), and Eczema Area and Severity Index (EASI) (SCORAD and EASI; S/E). However, the subjectivity of the diagnostic process remains limited. Hyperspectral cameras can detect optical signals to simultaneously generate morphological information and collect spatial information. The depth of skin penetration of caries can be determined according to the wavelength of light and its structural feature. Thus, after dividing the colors and information into six datasets, classified and analyzed their features and severities using representative diagnostic evaluation tools. First, the expansion of blood vessels and structural changes in the lesion area were confirmed between 550 and 650 nm, and information analysis of the lesion features was confirmed through the difference in the rate of change between the visible and near-infrared (NIR) areas. Subsequently, severity classification of the datasets for each wavelength of AD was performed. IGA-NIR-DenseNet121 showed the best classification performance, with 88% accuracy and S/E-G-ResNet18 showed 97% accuracy. Thus, IGA is sensitive to NIR, whereas SCORAD and EASI are sensitive to color. Although the overall accuracy was lower than existing RGB images, the t-distributed stochastic neighbor embedding results showed that the severity groups were well distinguished. The newly constructed dataset and severity classification study by wavelength suggested generalizations and methods for diagnosing and evaluating AD. Additionally, it shows the possibility of expanding research by identifying features that appear in the deep skin layers.https://ieeexplore.ieee.org/document/10720754/Hyperspectral imagingatopic dermatitisspectral-spatial featureseverityclassification |
| spellingShingle | Eun Bin Kim Yoo Sang Baek Onseok Lee A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging IEEE Access Hyperspectral imaging atopic dermatitis spectral-spatial feature severity classification |
| title | A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging |
| title_full | A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging |
| title_fullStr | A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging |
| title_full_unstemmed | A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging |
| title_short | A Study on the Classification of Atopic Dermatitis by Spectral Features of Hyperspectral Imaging |
| title_sort | study on the classification of atopic dermatitis by spectral features of hyperspectral imaging |
| topic | Hyperspectral imaging atopic dermatitis spectral-spatial feature severity classification |
| url | https://ieeexplore.ieee.org/document/10720754/ |
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