research Advancing Hair Disease Diagnostics: A Deep Learning Approach Using Inception-ResNet v2 for Multi-Class Classification
The model accurately identifies hair diseases using deep learning.
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research Automated Trichoscopic Analysis for Hair Disorder Classification Using Semantic Segmentation
An automated system can accurately classify hair disorders using image analysis.
research Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies hair diseases.
research FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Pre-trained classification of scalp conditions using image processing
The system can automatically classify scalp conditions with 85% accuracy.
research Development of a DNA-analytical Method for the Identification of Animal Hair Fibers in Textiles
Researchers developed a new method to identify animal hair in textiles, which is effective for various fibers and more reliable than previous methods.
research Classification and Frequency of Crown Whirlpool Patterns in Male Hair Transplant Patients
Understanding crown whorl patterns can improve hair transplant results for men.
research A discriminant multi-scale histopathology descriptor using dictionary learning
The new image descriptor helps identify skin cancer structures with good accuracy.
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 Wool Fibres: Sectioning and Staining, Differentiation of Ortho and Paracortex
research Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research Identification of Cashmere and Other Animal Hair Fibers in Textiles by MALDI-TOF Mass Spectrometry
Researchers developed a method to identify animal fibers in textiles, which works on processed and blended materials.
research Application of the basic and specific classification on patterned hair loss in Indians
The BASP classification is effective for diagnosing pattern hair loss in Indian men and women.
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 Rethinking pattern hair loss classification in the era of trichoscopy and artificial intelligence
A hybrid model using traditional methods, trichoscopy, and AI improves hair loss assessment.
research Liquid Chromatography-Mass Spectrometry Applied to The Identification And Quantification Of Animal Hair Fibers In Textile Products
Researchers developed a method to identify and measure different animal hair fibers in textiles, successfully distinguishing materials like cashmere from cheaper fibers.
research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition
The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
research Modified basic and specific (BASP) classification for pattern hair loss
The modified hair loss classification is more detailed but less user-friendly.
research A Simple Technique for Revealing the Surface Pattern of Hair
A simple method can show hair's surface pattern.
research Cortical cell types and intermediate filament arrangements correlate with fiber curvature in Japanese human hair
Hair curvature in Japanese people is linked to specific cell types and filament arrangements in the hair cortex.
research Hair and Scalp Disease Detection using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed images.
research Classifications of patterned hair loss: a review
Different types of hair loss need specific treatments, and while many classification systems exist, each has its flaws; more research is needed to refine these systems and treatments.
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 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 Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization Criminisi algorithm
The new hair removal algorithm for skin images works better for detecting and fixing hair, improving melanoma diagnosis.
research Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
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 Reliability of the pattern hair loss classifications: A comparison of the basic and specific and N orwood–H amilton classifications
The BASP classification is more reliable than the Norwood-Hamilton for classifying hair loss in men and women.
research Hair analysis based on medical history and spatial-temporal data
Machine learning can predict hair health accurately using personal data.