research A discriminant multi-scale histopathology descriptor using dictionary learning
The new image descriptor helps identify skin cancer structures with good accuracy.
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510-540 / 1000+ resultsresearch Multi-Class Support Vector Machine Classification for Detecting Alopecia Areata and Scalp Diseases
The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.
research Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
The model accurately detects early-stage hair loss using images.
research Bayesian measurement-error-driven hidden Markov regression model for calibrating the effect of covariates on multistate outcomes: Application to androgenetic alopecia
Model improves accuracy in predicting hair loss effects.
research Semantic textual similarity using maximal weighted bipartite graph matching
The gel effectively thickens eyelashes and eyebrows without side effects.
research CHROMALENS PRECISION MAPPING AS AN INNOVATIVE SYSTEM FOR MICROZONAL HAIR ANALYSIS: STRUCTURE, ALGORITHM, AND COMPARISON WITH CLASSICAL COLORING TECHNIQUES
research Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
A deep learning model accurately predicts male hair loss types using scalp images.
research Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
VGG19 is more accurate, but MobileNetV2 is faster and uses fewer resources.
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 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 Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN
An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.
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 Boundary-aware Multi-stage with Mobile U-Net for Hair Segmentation in Dermoscopic Images
The new method improves hair segmentation in skin images, helping detect skin cancer more accurately.
research Enhanced stratification of male pattern hair loss using AI through novel loss region ratio analysis
AI improves accuracy and consistency in diagnosing male pattern hair loss.
research A Comparative Analysis of Large Language Model Accuracy for Image-Based Hair Disease Identification in Diverse Skin Tones
ChatGPT is more accurate at diagnosing hair disorders in lighter skin tones than darker ones.
research An Approach to Detect Alopecia Areata Hair Disease Using Deep Learning
Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
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 Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
research 574 A deep learning classifier for the analysis of tiger tail banding in trichothiodystrophy
A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.
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 Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
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 Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
research BaldPredict: AI-Driven Prediction Model for Alopecia Areata
AI can improve alopecia areata diagnosis with high accuracy.
research AI06 ALUDWIG: an automated artificial intelligence-based assessment of female androgenic alopecia
ALUDWIG can help standardize female hair loss assessment from a single image.
research A Machine Learning Algorithm Applied to Trichoscopy for Androgenic Alopecia Staging and Severity Assessment
The machine learning model effectively assesses the severity of hair loss and could help dermatologists with treatment decisions.
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 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.