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.
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30 / 1000+ results research Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
Artificial neural networks can accurately diagnose Alopecia Areata.
research Hair & Scalp Disease Detection Using Machine Learning & Image Processing
AI can accurately diagnose hair and scalp conditions and suggest 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 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 Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
The model can effectively help diagnose meibomian gland dysfunction automatically.
research Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
research Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
research Evaluation of Automated Measurement of Hair Density Using Deep Neural Networks
Deep learning can accurately automate hair density measurement, with YOLOv4 performing best.
research A Machine Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
The system can automatically identify different hair and scalp conditions using machine learning.
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 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 A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Better datasets and methods are needed for reliable vitiligo detection using deep learning.
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 Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
Deep learning and explainable AI are improving scalp disorder diagnosis, but challenges in transparency and data quality remain.
research How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders
AI is effective in diagnosing and treating hair disorders, including detecting hair loss and scalp conditions with high accuracy, but it should supplement, not replace, doctor-patient interactions.
research Impact of artificial intelligence on healthcare
AI is revolutionizing healthcare by improving diagnosis, treatment, and monitoring, but still needs close supervision.
research ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.
research Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data
AI can improve early diagnosis and classification of PCOS, aiding in prevention of related health issues.
research Deep Learning-Based Dermatological Condition Detection: A Systematic Review With Recent Methods, Datasets, Challenges, and Future Directions
Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
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 Sample collecting methods for bacterial community structure analysis of scalp hair: non-invasive swabbing versus intrusive hair shaft cutting
Non-invasive swabbing is as effective as cutting hair for collecting scalp hair bacteria.
research A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
A web platform was created to help diagnose scalp conditions accurately and easily.
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 An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
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 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 Editorial: Progress and Prospects on Skin Imaging Technology, Teledermatology and Artificial Intelligence in Dermatology
New skin imaging, teledermatology, and AI could become key in future dermatology care.
research Innovative approaches to baldness detection and management with artificial intelligence and machine learning
research HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers
"HairSentinel" accurately detects hairfall trends using simple user data, helping identify health risks early.