research Melanosis Coli After Long-Term Ingestion of Cape Aloe
Long-term Cape Aloe use causes harmless colon pigmentation that can help detect polyps.
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research Faculty Opinions recommendation of A new classification of pattern hair loss that is universal for men and women: basic and specific (BASP) classification.
The BASP classification is a detailed system for categorizing hair loss in both men and women, but it may be complex for beginners and not fully suitable for grading female hair loss.
research Can we halt male androgenetic alopecia progression without antiandrogenic drugs?
Only anti-androgenic drugs likely halt AGA progression.
research 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 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 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.
research An Explainable AI-Based Decision Support System for Teaching and Classifying Hair Loss Types
The AI system accurately classifies hair loss types and explains its decisions.
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 Big Self-Supervised Models Advance Medical Image Classification
Self-supervised learning improves medical image classification accuracy.
research A comparative study of linear and nonlinear ANNs as classifiers of animal hair fibers
Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.
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 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 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 Role of Platelet-rich Plasma in the Management of Androgenetic Alopecia
PRP helps hair growth in common hair loss disorder.
research A Novel Fusion Technique for Early Detection of Alopecia Areata Using ResNet-50 and CRSHOG
AlopeciaDet accurately detects Alopecia Areata early using advanced image analysis.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
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 Amplitude-guided deep reinforcement learning for semi-supervised layer segmentation
The new method improves accuracy in segmenting scalp tissue layers.
research Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies hair diseases.
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 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 Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
research Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
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 Isomorphic and isotopic phenomenon occurring simultaneously in a case of granuloma annulare
A patient with granuloma annulare experienced both isotopic and isomorphic responses, with skin lesions responding to steroids but relapsing after stopping treatment.
research Historical Archives
The document reviews key historical figures and discoveries in dermatology.
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 Phenotype-Specific Lifestyle Prediction for PCOS Using Machine Learning Multi-Class Classification and SHAP Explainability
Machine learning can accurately predict PCOS phenotypes using lifestyle and symptom data.
research HERBSNAP: Advanced Classification of Bhringaraja and Honegone Herbal Species
Machine learning can accurately tell apart False Daisy and Smooth Joy Weed.