Skin Type Detection with Deep Learning: A Comparative Analysis Article Swipe
Fatma Betül Kara Ardaç
,
Resul Kara
,
Seda SAKACI ÇELİK
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.29130/dubited.930096
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.29130/dubited.930096
There are many factors that can change and affect appearance, including age and environment. Knowing the skin type helps to choose the products best suited to the needs of the skin and therefore the right skin care. Recently, the increasing demand for cosmetics and the scarcity of well-equipped cosmetologists have encouraged cosmetology centers to meet the need by using artificial intelligence applications. Deep learning applications can give high accuracy results in determining the skin type. Recent research shows that learning performs better on nonlinear data than machine learning methods. The aim of this study is to find the best classification model for skin type prediction in skin analysis data with deep learning. For this purpose, 4 different optimization algorithms as Sgd, Adagrad, Adam and Adamax; Tanh and ReLU activation functions and combinations of different neuron numbers using, 16 different models were created.In experimental studies, the performance of the models varies according to the parameters, and it has been observed that the most successful deep neural network model is the model consisting of 64 neurons, Sgd optimization function and ReLU activation function combination with a success rate of 93.75. The accuracy result obtained has a higher classification success compared to other methods, and shows that deep neural networks can make an accurate skin type classification.
Related Topics
Concepts
Deep learning
Artificial intelligence
Computer science
Activation function
Artificial neural network
Machine learning
Cosmetology
Scarcity
Convolutional neural network
Function (biology)
Skin care
Type (biology)
Pattern recognition (psychology)
Medicine
Economics
Microeconomics
Art
Ecology
Nursing
Evolutionary biology
Visual arts
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.29130/dubited.930096
- https://dergipark.org.tr/en/download/article-file/1741334
- OA Status
- diamond
- Cited By
- 4
- References
- 17
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4367185979
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4367185979Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.29130/dubited.930096Digital Object Identifier
- Title
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Skin Type Detection with Deep Learning: A Comparative AnalysisWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-27Full publication date if available
- Authors
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Fatma Betül Kara Ardaç, Resul Kara, Seda SAKACI ÇELİKList of authors in order
- Landing page
-
https://doi.org/10.29130/dubited.930096Publisher landing page
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https://dergipark.org.tr/en/download/article-file/1741334Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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diamondOpen access status per OpenAlex
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https://dergipark.org.tr/en/download/article-file/1741334Direct OA link when available
- Concepts
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Deep learning, Artificial intelligence, Computer science, Activation function, Artificial neural network, Machine learning, Cosmetology, Scarcity, Convolutional neural network, Function (biology), Skin care, Type (biology), Pattern recognition (psychology), Medicine, Economics, Microeconomics, Art, Ecology, Nursing, Evolutionary biology, Visual arts, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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4Total citation count in OpenAlex
- Citations by year (recent)
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2025: 3, 2024: 1Per-year citation counts (last 5 years)
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17Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| primary_location.source.host_organization_name | Düzce University |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310318352 |
| primary_location.source.host_organization_lineage_names | Düzce University |
| primary_location.license | cc-by-nc |
| primary_location.pdf_url | https://dergipark.org.tr/en/download/article-file/1741334 |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by-nc |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Düzce Üniversitesi Bilim ve Teknoloji Dergisi |
| primary_location.landing_page_url | https://doi.org/10.29130/dubited.930096 |
| publication_date | 2023-04-27 |
| publication_year | 2023 |
| referenced_works | https://openalex.org/W2915645771, https://openalex.org/W2620219072, https://openalex.org/W2105750284, https://openalex.org/W2323481414, https://openalex.org/W2774057992, https://openalex.org/W4231109964, https://openalex.org/W2805033630, https://openalex.org/W2971998616, https://openalex.org/W3030738016, https://openalex.org/W6760650454, https://openalex.org/W2942067081, https://openalex.org/W2965857035, https://openalex.org/W2953139847, https://openalex.org/W2595557940, https://openalex.org/W2761705332, https://openalex.org/W3104663419, https://openalex.org/W2946331359 |
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