research FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
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330-360 / 1000+ resultsresearch 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 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 Wool Fibres: Sectioning and Staining, Differentiation of Ortho and Paracortex
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 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 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 Hair casts: Classification, staining characteristics, and differential diagnosis
A new method helps identify and classify different types of hair casts.
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 Computer aided classification of female androgenetic alopecia-an image processing and analysis system
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 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 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 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 Modified basic and specific (BASP) classification for pattern hair loss
The modified hair loss classification is more detailed but less user-friendly.
research Lymphocytic mediated alopecia: histological classification by pattern analysis
Steven Kossard classified lymphocyte-related hair loss into four patterns, each linked to different types of baldness.
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 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 Investigating Different Deep learning Models for Classification of Folliculitis
GoogLeNet is the best model for identifying folliculitis.
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 Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification
The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.
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 Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research Wool fiber curvature is correlated with abundance of K38 and specific keratin‐associated proteins
Wool fiber curliness is linked to the presence of certain proteins and K38.
research Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
Computer vision techniques can help detect and assess skin conditions like vitiligo, alopecia areata, and dermatitis.
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 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.