Robust skin type classification using convolutional neural networks

Chang, Cheng-Chun; Hsing, Shi-Tien; Chuang, Yung-Chi; Wu, Chien-Ta; Fang, Tung-Jing; Chen, Kuan-Fu; Choi, B.W.

doi:10.1109/iciea.2018.8398040

Cited by 8 publications

(1 citation statement)

References 3 publications

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“…However, the dataset used in most of them was in the form of image and none was found to use categorical data in building the facial skin type prediction model and so no obtainable public dataset that could be used for this research was found. Research shows that manual data collection and labelling [9]is possible to be done and thus the same method of manually collecting and labelling dataset is used in this research. The dataset is distributed into 80% training data and 20% testing data [10].…”

Section: State Of the Artmentioning

confidence: 99%

Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning

et al. 2023

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The lack of knowledge of different facial skin types is still a frequent problem in Indonesia. The purpose of this research is to build a facial skin type prediction system using machine learning to classify facial skin types based on Baumann Skin Type Solutionswhich provides information on different skin types and suitable skincare ingredients. The dataset is collected manually by distributing a questionnaire among Indonesian citizens. The prediction models are built using three machine learning methods namely SVM, XGBoost, and 1D-CNN, and compared using 5-fold stratified cross-validation. XGBoostachieved the best performance on facial skin type prediction and optimized through hyperparameter tuning using Bayesian Optimization with a result of 93.5% averaged F1-score.

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Section: State Of the Artmentioning

confidence: 99%

Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning

et al. 2023

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Skin Type Diversity in Skin Lesion Datasets: A Review

Alipour,

Burke,

Courtney

2024

Curr Derm Rep

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Purpose of review Skin type diversity in image datasets refers to the representation of various skin types. This diversity allows for the verification of comparable performance of a trained model across different skin types. A widespread problem in datasets involving human skin is the lack of verifiable diversity in skin types, making it difficult to evaluate whether the performance of the trained models generalizes across different skin types. For example, the diversity issues in skin lesion datasets, which are used to train deep learning-based models, often result in lower accuracy for darker skin types that are typically under-represented in these datasets. Under-representation in datasets results in lower performance in deep learning models for under-represented skin types. Recent findings This issue has been discussed in previous works; however, the reporting of skin types, and inherent diversity, have not been fully assessed. Some works report skin types but do not attempt to assess the representation of each skin type in datasets. Others, focusing on skin lesions, identify the issue but do not measure skin type diversity in the datasets examined. Summary Effort is needed to address these shortcomings and move towards facilitating verifiable diversity. Building on previous works in skin lesion datasets, this review explores the general issue of skin type diversity by investigating and evaluating skin lesion datasets specifically. The main contributions of this work are an evaluation of publicly available skin lesion datasets and their metadata to assess the frequency and completeness of reporting of skin type and an investigation into the diversity and representation of each skin type within these datasets.

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Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning

Tadesse,

Cintas,

Varshney

et al. 2023

npj Digit. Med.

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Images depicting dark skin tones are significantly underrepresented in the educational materials used to teach primary care physicians and dermatologists to recognize skin diseases. This could contribute to disparities in skin disease diagnosis across different racial groups. Previously, domain experts have manually assessed textbooks to estimate the diversity in skin images. Manual assessment does not scale to many educational materials and introduces human errors. To automate this process, we present the Skin Tone Analysis for Representation in EDucational materials (STAR-ED) framework, which assesses skin tone representation in medical education materials using machine learning. Given a document (e.g., a textbook in .pdf), STAR-ED applies content parsing to extract text, images, and table entities in a structured format. Next, it identifies images containing skin, segments the skin-containing portions of those images, and estimates the skin tone using machine learning. STAR-ED was developed using the Fitzpatrick17k dataset. We then externally tested STAR-ED on four commonly used medical textbooks. Results show strong performance in detecting skin images (0.96 ± 0.02 AUROC and 0.90 ± 0.06 F1 score) and classifying skin tones (0.87 ± 0.01 AUROC and 0.91 ± 0.00 F1 score). STAR-ED quantifies the imbalanced representation of skin tones in four medical textbooks: brown and black skin tones (Fitzpatrick V-VI) images constitute only 10.5% of all skin images. We envision this technology as a tool for medical educators, publishers, and practitioners to assess skin tone diversity in their educational materials.

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Robust skin type classification using convolutional neural networks

Cited by 8 publications

References 3 publications

Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning

Facial Skin Type Prediction Based on Baumann Skin Type Solutions Theory Using Machine Learning

Skin Type Diversity in Skin Lesion Datasets: A Review

Skin Tone Analysis for Representation in Educational Materials (STAR-ED) using machine learning

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