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Related Experiment Video

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Human skin type classification using image processing and deep learning approaches.

Sirawit Saiwaeo1, Sujitra Arwatchananukul1,2, Lapatrada Mungmai3,4

  • 1School of Information Technology, Mae Fah Luang University, Chiang Rai, Thailand.

Heliyon
|November 29, 2023
PubMed
Summary
This summary is machine-generated.

This study developed an AI model using Convolutional Neural Networks (CNNs) to accurately classify skin types from images. The EfficientNet-V2 model achieved high accuracy, aiding consumers in selecting appropriate cosmetic products.

Keywords:
CNNContrast limited adaptive histogram equalization (CLAHE)Data preparationImage augmentationImage enhancementSkin images

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Area of Science:

  • Dermatology
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate skin type identification is crucial for cosmetic product selection.
  • Variations in skin type (oily, dry, normal) can complicate self-assessment.
  • Artificial intelligence (AI) and machine learning (ML) offer potential solutions for objective classification.

Purpose of the Study:

  • To develop and evaluate a deep learning model for automated skin type classification.
  • To compare the performance of various Convolutional Neural Network (CNN) architectures.
  • To optimize a CNN model for improved accuracy in skin type identification.

Main Methods:

  • A dataset of normal, oily, and dry skin images was curated.
  • Image preprocessing included Contrast Limited Adaptive Histogram Equalization (CLAHE) and data augmentation.
  • Several CNN architectures (MobileNet-V2, EfficientNet-V2, InceptionV2, ResNet-V1) were trained and optimized.
  • Hyperparameter tuning and 10-fold cross-validation were employed for robust evaluation.

Main Results:

  • The EfficientNet-V2 architecture demonstrated superior performance, achieving 91.55% accuracy.
  • Hyperparameter tuning further improved the model's accuracy to 94.57%.
  • The final model achieved 89.70% accuracy on unseen data, indicating strong generalization.

Conclusions:

  • AI-powered skin type classification using CNNs is a viable and accurate approach.
  • The developed model can assist consumers in making informed decisions about cosmetic products.
  • Further research can explore larger datasets and diverse populations for enhanced model robustness.