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- A Novel Fusion Technique for Early Detection of Alopecia Areata Using ResNet-50 and CRSHOG
- Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
- Rejuvenation by Design: Building Cosmetic Products with Extracellular Vesicle Technology, the Example of Exo.Reset®
- Homeodynamic Rejuvenation: an adaptive framework for resetting skin aging
- Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet v2 for Multi-Class Classification
- Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
- Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
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