December 2022 in “Research Square (Research Square)” This study discusses the development of deep learning models for diagnosing skin disorders and notes challenges such as lack of data for darker skin tones, without providing new clinical results.
5 citations
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January 2025 in “BMC Medical Informatics and Decision Making” This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.
3 citations
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February 2024 in “arXiv (Cornell University)” In this study, researchers used Google Search ads to gather an open access dataset of 10,408 dermatological images from over 5,000 U.S. internet users, enhancing the diversity and representativeness of skin condition images available for research and artificial intelligence development.
61 citations
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June 2022 in “IEEE Journal of Biomedical and Health Informatics” This study introduced a novel deep clustering approach for melanoma detection from dermoscopic images, demonstrating improved performance over existing methods by mitigating class imbalance issues using a center-oriented margin-free triplet loss.
1 citations
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January 2023 in “IEEE access” This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.