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.
This study developed a convolutional neural network model for non-invasive diagnosis of androgenetic alopecia using dermoscopic images, demonstrating potential accuracy and scalability while highlighting the importance of model interpretability for clinical use.
July 2025 in “Harvard Dataverse” A deep learning model accurately detects early hair loss signs using scalp images.
1 citations
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November 2003 in “Humana Press eBooks” This article discusses the use of atomic force microscopy for examining human hair surfaces in dermatology, cosmetics, and forensic science, but presents no new research findings.
4 citations
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May 2024 in “INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT” This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.
