research Hair Loss Diagnosis Using Artificial Neural Networks
Neural networks can effectively predict hair loss.
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30 / 1000+ results research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
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 Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images
Deep learning can improve non-invasive alopecia diagnosis using hair images.
research Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
research A mechanism-informed deep neural network enables prioritization of regulators that drive cell state transitions
A new neural network helps identify key regulators in cell changes, aiding in understanding diseases and finding new treatments.
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 The Dark Side of the Language: Syntax-Based Neural Networks Rivaling Transformers in Definitely Unseen Sentences
Syntax-based neural networks can match Transformers in handling unseen sentences.
research 42063 Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images
The model can effectively identify good quality skin images but needs more testing for real-world use.
research TTNPB Promotes Human Pluripotent Stem Cell‐to‐Neural Stem Cell Transition via Modulation of Chromatin Accessibility and the S‐(5′‐adenosyl)‐L‐homocysteine/Choline Metabolic Network
TTNPB helps turn stem cells into neural stem cells, improving depression-like behaviors in rats.
research Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
A new model can predict drug-disease links well, helping drug research.
research Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
The model can effectively help diagnose meibomian gland dysfunction automatically.
research DNN-DTIs: improved drug-target interactions prediction using XGBoost feature selection and deep neural network
The DNN-DTIs method accurately predicts drug-target interactions and is useful for drug repositioning.
research In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
research Baseline Neutrophil-to-Lymphocyte Ratio Stratifies Early Trichoscopic Response to Platelet-Rich Plasma–Based Regimens in Non-Scarring Alopecia: A Real-World Cohort with Internal Validation Using an Interpretable Neural Network
A high neutrophil-to-lymphocyte ratio may predict poor response to hair loss treatment.
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 Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
AI can predict hair loss by analyzing genetic, scalp, and lifestyle data.
research Innervated Biomaterials for Improved Cutaneous Wound Healing: A Review of Recent Advancements and Future Prospects
New biomaterials can improve wound healing by promoting nerve and tissue regeneration.
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 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 203 Automated skin surface phenotype for melanoma risk assessment
An automated method accurately assesses melanoma risk using 3D body images to analyze skin traits.
research Hair cluster detection model based on dermoscopic images
The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
research Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
The Advanced Precipitation U-Net Model improves early hair fall detection with 92% accuracy.
research 1040 Photographing alopecia - How many pixels are needed for clinical evaluation?
Higher resolution images are needed to identify scarring and fine hair in alopecia.
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 Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
Researchers developed an algorithm for self-diagnosing scalp conditions with high accuracy using smart device-attached microscopes.
research Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest treatments.
research SNP variation in male pattern hair loss in Russians with different dihydrotestosterone levels
Certain gene variations increase male hair loss risk, influenced by hormone levels.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
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.