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

Eun Bin Kim¹, Yoo Sang Baek², Onesok Lee^1,3

¹Department of Software Convergence, Graduate School, Soonchunhyang University, Asan City, Chungcheongnam-do, South Korea.

Skin Research and Technology : Official Journal of International Society for Bioengineering and the Skin (ISBS) [And] International Society for Digital Imaging of Skin (ISDIS) [And] International Society for Skin Imaging (ISSI)

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Summary

This summary is machine-generated.

Hyperspectral imaging (HSI) effectively classifies atopic dermatitis (AD) severity using transfer learning. Training models on psoriasis data achieved high accuracy, demonstrating HSI

Keywords:

atopic dermatitis domain selection hyperspectral imaging severity classification transfer learning

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Area of Science:

Dermatology
Medical Imaging
Machine Learning

Background:

Atopic dermatitis (AD) is a chronic inflammatory skin condition with structural changes.
Noninvasive methods are needed to evaluate AD.
Hyperspectral imaging (HSI) captures skin structural features using light wavelength variations.

Purpose of the Study:

To classify the severity of atopic dermatitis (AD) using hyperspectral imaging (HSI).
To optimize classification results by exploring different source- and target-domain datasets with parameter-based transfer learning.

Main Methods:

Utilized psoriasis, skin cancer, eczema, and AD datasets as source domains.
Employed hyperspectral images as the target domain for wavelength-specific AD classification.
Evaluated 96 combinations of sources, models, and targets for severity classification performance.

Main Results:

Achieved 83% classification performance using ResNet50 trained on an augmented psoriasis dataset (source) and NIR dataset (target).
Obtained 81% accuracy with ResNet50 trained on an unaugmented psoriasis dataset (source) and R dataset (target).
ResNet50 showed potential as a generalized model, and the psoriasis dataset proved effective for training.

Conclusions:

Demonstrated the feasibility of classifying AD severity using hyperspectral images.
Showcased the scalability of hyperspectral imaging and transfer learning for domain exploration in AD research.