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%.
Search
for
Sort by
Research
30 / 1000+ results
research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.
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 Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
The model accurately detects early-stage hair loss using images.
research Investigating Different Deep learning Models for Classification of Folliculitis
GoogLeNet is the best model for identifying folliculitis.
research 200 Deep learning model to predict sentinel lymph node status in melanoma patients
The AI model somewhat predicts lymph node status in melanoma patients using skin sample images.
research DEEP REINFORCEMENT LEARNING FOR SCALABLE CONTROL OF BOOLEAN MODELS IN THE CONTEXT OF CELLULAR REPROGRAMMING
pbn-STAC effectively finds strategies for cellular reprogramming using deep reinforcement learning.
research Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
Researchers improved a skin disease diagnosis model using online images, achieving up to 49.64% accuracy.
research Exploring machine learning strategies for single-cell transcriptomic analysis in wound healing
Machine learning and single-cell analysis improve understanding and treatment of wound healing.
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 Severity Prediction of Hair Loss in Alopecia Areata with Hybrid Deep Learning Architecture
The model accurately predicts hair loss severity in alopecia areata.
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 Enhanced Hair Disease Classification Using Deep Learning
The model accurately classifies hair conditions with 97% accuracy.
research FRCNN based Deep Learning for Identification and Classification of Alopecia Areata
The model accurately detects alopecia areata with 84.3% accuracy.
research A DEEP LEARNING APPROACH FOR DIAGNOSIS OF COVID-19 INFECTION AND ITS RELATED FACTORS: A POPULATION-BASED STUDY
The random forest model effectively helps diagnose COVID-19 using key factors like age and symptoms.
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 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 Hair Color Digitization through Imaging and Deep Inverse Graphics
A new method accurately captures and renders hair color for real and synthetic images.
research Analyse trichoskopischer Bilder mit tiefen neuronalen Netzen zur Diagnose und Aktivitätsbewertung von Alopecia areata – eine retrospektive Studie
Deep-learning models can effectively diagnose and assess Alopecia areata using scalp images.
research Using artificial intelligence to compute Severity of Alopecia Tool scores
AI can accurately measure hair loss severity in alopecia areata.
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 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 Deep Learning-based Automatic System for Diagnosis and Classification of Skin Dermatoses
The document concludes that an automatic system using deep learning can help diagnose skin disorders, but challenges and opportunities in this area remain.
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 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 Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
Deep learning effectively diagnoses scalp disorders, but improvements are needed.
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 Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
AI can predict hair loss by analyzing genetic, scalp, and lifestyle data.
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 Deep Learning Based Non-Invasive Framework for Nutritional Deficiency Detection Using Hair and Nail Images
A deep learning method can detect nutritional deficiencies from hair and nail images with 89% accuracy.