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.
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30-60 / 1000+ results research CircRNA-1926 Promotes the Differentiation of Goat SHF Stem Cells into Hair Follicle Lineage by miR-148a/b-3p/CDK19 Axis
circRNA-1926 helps goat stem cells turn into hair follicles by affecting miR-148a/b-3p and CDK19.
research A modified Sobel filter-based automated numerical algorithm enables immediate trichoscopic assessment of hair diameter diversity in male and female pattern hair loss
A new computer tool quickly measures hair thickness differences in people with common types of hair loss.
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 Identification and expression of the target gene SLC24A2 of oar-miR-377 and its novel SNPs effects on wool traits in sheep
The TT genotype of a specific SNP in sheep is linked to better wool quality.
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 Screened of long non-coding RNA related to wool development and fineness in Gansu alpine fine-wool sheep
Long non-coding RNAs help regulate wool fineness in Gansu alpine fine-wool sheep.
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 Validation of alopecia coding in US claims data among women of childbearing age
The algorithm accurately identified alopecia in women of childbearing age using claims data.
research DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.
research Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
The new model improves Alopecia Areata classification accuracy to 93.1%.
research Genome Wide Differential Expression Profiles in Nevus Sebaceous Uncovered Low Expression of CDKN2AIP and Construction of a ceRNA Network
CDKN2AIP gene is less active in nevus sebaceous, affecting related RNA networks.
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 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 The Cohen hair loss classification system
Dr. Bernard Cohen created a new system to classify hair loss using numbers and a detailed scalp map.
research NLKFill: high-resolution image inpainting with a novel large kernel attention
NLKFill improves high-resolution image inpainting by effectively capturing image details and enhancing speed.
research Research on Mental Stress Recognition of Depressive Disorders in Patients With Androgenic Alopecia Based on Machine Learning and Fuzzy K-Means Clustering
The FAW-FS algorithm improves depression recognition, and psychological interventions help AGA patients' mental health.
research Using morphological operators and inpainting for hair removal in dermoscopic images
The new algorithm removes hair from skin images better than previous methods, helping diagnose melanoma.
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 Systematic Analysis of Non-coding RNAs Involved in the Angora Rabbit (Oryctolagus cuniculus) Hair Follicle Cycle by RNA Sequencing
Non-coding RNAs play key roles in the hair growth cycle of Angora rabbits.
research Cost-effectively dissecting the genetic architecture of complex wool traits in rabbits by low-coverage sequencing
Low-coverage sequencing is a cost-effective way to identify genes related to wool traits in rabbits.
research Computer aided classification of female androgenetic alopecia-an image processing and analysis system
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 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 Enhancing Hairfall Prediction: A Comparative Analysis of Individual Algorithms and An Ensemble Method
Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
research 1394 Long non-coding RNA AL136131.3 inhibits hair growth through mediating glycolysis in androgenetic alopecia
AL136131.3 slows hair growth by affecting energy processes in hair loss.
research Mind the Gap: A Solution to the Robustness Problem of Turing Patterns Through Patterning Mode Isolation
Pattern mode isolation improves the reliability and predictability of Turing patterns.
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 Understanding hair disorders through artificial intelligence: DALLE's role in image creation
DALL-E 2 can create realistic hair images but struggles with specific hair disorders.
research Comparative Analysis of Machine Learning Algorithms in Diagnosis of Polycystic Ovarian Syndrome
The Random Forest algorithm was the most accurate at diagnosing Polycystic Ovarian Syndrome.