AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation
Traditional methods for skin color classification, such as visual assessments and conventional image classification, face limitations in accuracy and consistency under varying conditions. To address this, we developed AI Dermatochroma Analytica (AIDA), an unsupervised learning system designed to enh...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2024-12-01
|
| Series: | Cosmetics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9284/11/6/218 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1846105162881433600 |
|---|---|
| author | Abderrachid Hamrani Daniela Leizaola Nikhil Kumar Reddy Vedere Robert S. Kirsner Kacie Kaile Alexander Lee Trinidad Anuradha Godavarty |
| author_facet | Abderrachid Hamrani Daniela Leizaola Nikhil Kumar Reddy Vedere Robert S. Kirsner Kacie Kaile Alexander Lee Trinidad Anuradha Godavarty |
| author_sort | Abderrachid Hamrani |
| collection | DOAJ |
| description | Traditional methods for skin color classification, such as visual assessments and conventional image classification, face limitations in accuracy and consistency under varying conditions. To address this, we developed AI Dermatochroma Analytica (AIDA), an unsupervised learning system designed to enhance dermatological diagnostics. AIDA applies clustering techniques to classify skin tones without relying on labeled data, evaluating over twelve models, including K-means, density-based, hierarchical, and fuzzy logic algorithms. The model’s key feature is its ability to mimic the process clinicians traditionally perform by visually matching the skin with the Fitzpatrick Skin Type (FST) palette scale but with enhanced precision and accuracy using Euclidean distance-based clustering techniques. AIDA demonstrated superior performance, achieving a 97% accuracy rate compared to 87% for a supervised convolutional neural network (CNN). The system also segments skin images into clusters based on color similarity, providing detailed spatial mapping aligned with dermatological standards. This segmentation reduces the uncertainty related to lighting conditions and other environmental factors, enhancing precision and consistency in skin color classification. This approach offers significant improvements in personalized dermatological care by reducing reliance on labeled data, improving diagnostic accuracy, and paving the way for future applications in diverse dermatological and cosmetic contexts. |
| format | Article |
| id | doaj-art-bd9abf4be45743db9d055fc9af317a62 |
| institution | Kabale University |
| issn | 2079-9284 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Cosmetics |
| spelling | doaj-art-bd9abf4be45743db9d055fc9af317a622024-12-27T14:19:24ZengMDPI AGCosmetics2079-92842024-12-0111621810.3390/cosmetics11060218AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and SegmentationAbderrachid Hamrani0Daniela Leizaola1Nikhil Kumar Reddy Vedere2Robert S. Kirsner3Kacie Kaile4Alexander Lee Trinidad5Anuradha Godavarty6Department of Mechanical and Materials Engineering, Florida International University, Miami, FL 33174, USAOptical Imaging Laboratory, Department of Biomedical Engineering, Florida International University, 10555 West Flagler Street, EC 2675, Miami, FL 33174, USAOptical Imaging Laboratory, Department of Biomedical Engineering, Florida International University, 10555 West Flagler Street, EC 2675, Miami, FL 33174, USADepartment of Dermatology & Cutaneous Surgery, University of Miami Miller School of Medicine, Miami, FL 33174, USAOptical Imaging Laboratory, Department of Biomedical Engineering, Florida International University, 10555 West Flagler Street, EC 2675, Miami, FL 33174, USAOptical Imaging Laboratory, Department of Biomedical Engineering, Florida International University, 10555 West Flagler Street, EC 2675, Miami, FL 33174, USAOptical Imaging Laboratory, Department of Biomedical Engineering, Florida International University, 10555 West Flagler Street, EC 2675, Miami, FL 33174, USATraditional methods for skin color classification, such as visual assessments and conventional image classification, face limitations in accuracy and consistency under varying conditions. To address this, we developed AI Dermatochroma Analytica (AIDA), an unsupervised learning system designed to enhance dermatological diagnostics. AIDA applies clustering techniques to classify skin tones without relying on labeled data, evaluating over twelve models, including K-means, density-based, hierarchical, and fuzzy logic algorithms. The model’s key feature is its ability to mimic the process clinicians traditionally perform by visually matching the skin with the Fitzpatrick Skin Type (FST) palette scale but with enhanced precision and accuracy using Euclidean distance-based clustering techniques. AIDA demonstrated superior performance, achieving a 97% accuracy rate compared to 87% for a supervised convolutional neural network (CNN). The system also segments skin images into clusters based on color similarity, providing detailed spatial mapping aligned with dermatological standards. This segmentation reduces the uncertainty related to lighting conditions and other environmental factors, enhancing precision and consistency in skin color classification. This approach offers significant improvements in personalized dermatological care by reducing reliance on labeled data, improving diagnostic accuracy, and paving the way for future applications in diverse dermatological and cosmetic contexts.https://www.mdpi.com/2079-9284/11/6/218skin color classificationmachine learning unsupervised clustering |
| spellingShingle | Abderrachid Hamrani Daniela Leizaola Nikhil Kumar Reddy Vedere Robert S. Kirsner Kacie Kaile Alexander Lee Trinidad Anuradha Godavarty AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation Cosmetics skin color classification machine learning unsupervised clustering |
| title | AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation |
| title_full | AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation |
| title_fullStr | AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation |
| title_full_unstemmed | AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation |
| title_short | AI Dermatochroma Analytica (AIDA): Smart Technology for Robust Skin Color Classification and Segmentation |
| title_sort | ai dermatochroma analytica aida smart technology for robust skin color classification and segmentation |
| topic | skin color classification machine learning unsupervised clustering |
| url | https://www.mdpi.com/2079-9284/11/6/218 |
| work_keys_str_mv | AT abderrachidhamrani aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation AT danielaleizaola aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation AT nikhilkumarreddyvedere aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation AT robertskirsner aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation AT kaciekaile aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation AT alexanderleetrinidad aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation AT anuradhagodavarty aidermatochromaanalyticaaidasmarttechnologyforrobustskincolorclassificationandsegmentation |