has been cited by the following article(s):
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[1]
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A novel hybrid deep learning technique for segmentation and classification of skin diseases: A review analysis and proposed approach
FIRST INTERNATIONAL CONFERENCE ON ADVENT TRENDS IN COMPUTATIONAL INTELLIGENCE AND COMMUNICATION TECHNOLOGIES: ICATCICT2024,
2025
DOI:10.1063/5.0289494
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[2]
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Photonics-powered augmented reality skin electronics for proactive healthcare: multifaceted opportunities
Microchimica Acta,
2024
DOI:10.1007/s00604-024-06314-3
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[3]
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A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases
Journal of Imaging Informatics in Medicine,
2024
DOI:10.1007/s10278-024-01300-w
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[4]
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Improving Access to Skin Care with a Multi-Factorial Strategy Using Vision Transformers
2024 4th International Conference on Sustainable Expert Systems (ICSES),
2024
DOI:10.1109/ICSES63445.2024.10763327
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[5]
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A Multi-model Deep Learning Architecture for Diagnosing Multi-class Skin Diseases
Journal of Imaging Informatics in Medicine,
2024
DOI:10.1007/s10278-024-01300-w
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[6]
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Ensembling Transfer Learning Frameworks for Effective Lightweight Skin Disease Detection
2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI),
2023
DOI:10.1109/ICAIIHI57871.2023.10489057
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[7]
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FDLM: An enhanced feature based deep learning model for skin lesion detection
Multimedia Tools and Applications,
2023
DOI:10.1007/s11042-023-17143-6
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[8]
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Ensembling Transfer Learning Frameworks for Effective Lightweight Skin Disease Detection
2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI),
2023
DOI:10.1109/ICAIIHI57871.2023.10489057
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[9]
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FDLM: An enhanced feature based deep learning model for skin lesion detection
Multimedia Tools and Applications,
2023
DOI:10.1007/s11042-023-17143-6
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[10]
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Recognition of human skin diseases using inception-V3 with transfer learning
International Journal of Information Technology,
2022
DOI:10.1007/s41870-022-01050-4
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