2 citations
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January 2024 in “IEEE Access” This study introduces AlopeciaDet, a novel feature fusion technique, using camera images to detect Alopecia Areata with 99.45% accuracy, outperforming existing methods by leveraging CRSHOG and ResNet-50 features.
This study found that a deep learning framework using the ResNet50 model achieved 95% overall accuracy in classifying 10 categories of hair diseases, demonstrating reliable performance but also identifying potential improvements due to misclassifications between similar conditions.
April 2026 in “IntechOpen eBooks” The researchers presented a case study on Exo.Reset®, a cosmetic formulation using extracellular vesicle technology, demonstrating systematic application of recognized standards to ensure scientific rigor and regulatory compliance in skin rejuvenation product development, providing a practical model for EV-based cosmetic innovations.
June 2026 in “Frontiers in Aging” This study introduces "Homeodynamic Rejuvenation" as a method to restore the skin's resilience by enhancing its ability to detect and recover from stress, focusing on five core biological processes critical for maintaining skin function and health amidst environmental and metabolic challenges.
This study developed a high-performance deep learning model using the Inception-ResNet v2 architecture to classify 10 hair disease classes, achieving an accuracy of 94.7% and balanced precision, recall, and F1-scores of 0.94, suggesting reliability for automated dermatology diagnostics.