research A Novel Fusion Technique for Early Detection of Alopecia Areata Using ResNet-50 and CRSHOG
AlopeciaDet accurately detects Alopecia Areata early using advanced image analysis.
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30 / 1000+ resultsresearch Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
The model accurately diagnoses hair diseases with 95% accuracy using deep learning.
research Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
VGG16 and VGG19 are the most accurate for classifying scalp and hair diseases.
research A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
The system effectively detects scalp diseases and classifies hair fall stages with high precision.
research HairLossMultinet: A Multi Scale Feature Fusion Method Using Deep Learning Approach
HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.
research Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies hair diseases.
research Investigating Different Deep learning Models for Classification of Folliculitis
GoogLeNet is the best model for identifying folliculitis.
research Big Self-Supervised Models Advance Medical Image Classification
Self-supervised learning improves medical image classification accuracy.
research Generative adversarial networks for anonymous acneic face dataset generation
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
research Androgenic Alopecia: Cross-Talk Between Cell Signal Transduction Pathways
Hair loss in Androgenetic alopecia (AGA) is due to altered cell sensitivity to hormones, not increased hormone levels. Hair growth periods shorten over time, causing hair to become thinner and shorter. This is linked to miscommunication between cell pathways in hair follicles. There's also a change in gene expression related to blood vessels and cell growth in balding hair follicles. The exact molecular causes of AGA are still unclear.
research Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
The new method improves skin cancer detection in imbalanced datasets.
research AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
research A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Better datasets and methods are needed for reliable vitiligo detection using deep learning.
research Weakly-Supervised Hair SEM Microscope Image Segmentation Using a Priori Structure Information
The method improves hair image segmentation accuracy while reducing annotation costs.
research 448 Challenges of securing insurance approval for oral tofacitinib treatment of alopecia areata: A multi-institutional retrospective review
Getting insurance to cover the hair loss treatment tofacitinib is hard because it's not officially approved for that use.
research A new mutation Rim3 resembling Re den is mapped close to retinoic acid receptor alpha (Rara) gene on mouse Chromosome 11
research Forme létale de syndrome de Netherton au sein d’une famille multiplex consanguine
A severe form of Netherton syndrome caused by a specific gene mutation led to neonatal deaths in a family.
research 565 Cutaneous delivery of LEKTI via an engineered strain of staphylococcus epidermidis for the treatment of netherton syndrome
ATR12-351 ointment safely delivers LEKTI protein to the skin, reducing enzyme activity in Netherton syndrome.
research [RETRACTED] Restolin Reviews Amazon: UK, USA, Australia, South Africa, Canada, NZ (Ingredients Updates) v1
The document is retracted and cannot be summarized.
research Sesn2/AMPK/mTOR signaling mediates balance between survival and apoptosis in sensory hair cells under stress
Sesn2 protects inner ear hair cells from damage by regulating certain cell survival pathways.
research Efficacy and safety evaluations of topical proretinal nanoparticles
Proretinal nanoparticles are a safe and effective way to deliver retinal to the skin.
research PULMONARY DELIVERY OF RESVERATROL-LOADED NANOCOMPOSITE MICROPARTICLES TO TREAT LUNG CANCER
Resveratrol-loaded nanoparticles show promise for lung cancer treatment.
research A SUBPOPULATION OF ITCH RECEPTORS MARKED BY RET EXPRESSION
A new type of nerve cell involved in itch perception was discovered.
research Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
research Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
research Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging
A new tool can analyze hair to detect changes due to hormones, genetics, and aging.
research Co-delivering Retinol and Niacinamide via Liposomes: Enhanced Stability, Efficient Skin Permeation, and Improved Anti-Aging/Skin-Brightening Efficacy
The new delivery system makes retinol and niacinamide more stable and effective for anti-aging and skin-brightening.
research Poster Presentations
Ustekinumab successfully treated a man's resistant skin condition when other treatments failed.
research A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
A web platform was created to help diagnose scalp conditions accurately and easily.