research Scalp disease analysis using deep learning models
Deep learning models can analyze scalp diseases effectively.
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30 / 1000+ results 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 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 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 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 PEDL-XAI: A Hybrid Probabilistic Ensemble Deep Learning Approach for Hair Disorder Diagnosis
The new AI system accurately diagnoses hair disorders and offers personalized treatment recommendations.
research Past, present, and future of global research on artificial intelligence applications in dermatology: A bibliometric analysis
AI in dermatology is growing rapidly, showing promise in diagnosing skin conditions as accurately as dermatologists.
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 Quantitative analysis and development of alopecia areata classification frameworks
A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
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 Analysis of modern approaches to hair care: An overview of products and techniques
Personalized hair care using modern techniques and science is essential for healthy hair.
research Analysis of modern approaches to hair care: An overview of products and techniques
Personalized hair care plans are essential for healthy hair.
research Genetic and Environmental Factors Affecting Hair Density in East Asian Populations
Age, sex, BMI, menopause, and specific genes affect hair density in East Asians.
research PandaOmics: An AI-Driven Platform for Therapeutic Target and Biomarker Discovery
PandaOmics uses AI to find new disease treatment targets and biomarkers.
research Analysis of trichoscopic images using deep neural networks for the diagnosis and activity assessment of alopecia areata – a retrospective study
AI can accurately diagnose and assess alopecia areata using scalp images.
research A Hybrid Deep Learning System for Automatic Detection of Scalp Diseases and Hair Fall Stage Classification
The system effectively detects scalp diseases and classifies hair fall stages with high precision.
research Neurospectrum: A Geometric and Topological Deep Learning Framework for Uncovering Spatiotemporal Signatures in Neural Activity
Neurospectrum effectively analyzes neural signals to predict and identify brain activity patterns better than traditional methods.
research The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline
The Hair Cell Analysis Toolbox automates and improves the analysis of cochlear hair cells using machine learning.
research Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging
A new tool can analyze hair to detect changes due to hormones, genetics, and aging.
research Research Techniques Made Simple: Optical Clearing and Three-Dimensional Volumetric Imaging of Skin Biopsies
3D imaging of skin biopsies offers better accuracy but is time-consuming and can't clear melanin.
research Hair and scalp disease detection using deep learning
Deep learning can effectively detect hair and scalp diseases early.
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 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 Artificial Intelligence Deep Learning Ultrasound Discrimination of Cosmetic Fillers
AI can accurately identify some cosmetic fillers in ultrasound images but needs improvement for others.
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 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 Automated Machine Learning Analysis of Patients With Chronic Skin Disease Using a Medical Smartphone App: Retrospective Study
Machine learning can predict symptoms and quality of life in chronic skin disease patients using smartphone app data, and shows that app use varies with patient characteristics.
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 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 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.