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Japanese women's curved hair has an uneven internal structure and varied amino acid composition.

Hair curliness is due to uneven distribution of different cortices within the hair fiber.

Leg hair can be used for eyelash transplants, resulting in fuller lashes with less maintenance.

Hair transplant surgery successfully restored a woman's pubic hair, improving her appearance and satisfaction.

SELP::KP improves hair strength, elasticity, and health, making it a promising hair cosmetic.

Transplanting beard hair into a scalp wound after hair transplant surgery improves scar appearance and hair keeps its normal traits.

A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.

The system can accurately classify hair diseases with 94.5% accuracy using a CNN.

The model accurately classifies hair conditions with 97% accuracy.

Transfer learning with three neural network architectures accurately classifies hair diseases.

A 6-group geometric classification of human scalp hair is more reliable and objective for testing than an 8-group system.

Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.

Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.

A deep learning model accurately predicts male hair loss types using scalp images.

Curved human hair has different structures on its convex and concave sides.

The model accurately identifies hair diseases using deep learning.

A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.

The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.

VGG19 is more accurate, but MobileNetV2 is faster and uses fewer resources.

The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.

The AI system accurately classifies hair loss types and explains its decisions.

The model accurately predicts hair loss severity in alopecia areata.

The model accurately diagnoses hair diseases with 95% accuracy using deep learning.

The research found that twisting hair fibers can show changes in stiffness and damage, and help tell apart different hair treatments.

Hair curvature is the main factor affecting curly hair manageability and product preferences.

The system can automatically classify scalp conditions with 85% accuracy.

Keratoacanthoma and some squamous cell carcinomas are linked to hair follicles, while others are not.

The system effectively detects scalp diseases and classifies hair fall stages with high precision.

Curved human hair has different structures on each side, which might cause its shape and is similar to wool.

Hair shaft changes may be linked to CCCA, but their role is unclear.