Search
for
Sort by
Research
810-840 / 1000+ results
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 DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
AI can effectively detect hair and scalp disorders from images.
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 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 Prediction of Alopecia Areata using CNN
A new CNN model can detect Alopecia Areata with 98% accuracy.
research MSF-VMDNet for multi class segmentation of skin cancer whole slide images using a multi frequency dual encoder network
MSF-VMDNet accurately segments skin cancer images better than existing methods.
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 Prescribers alerted to potential adverse effects of finasteride and montelukast
research 10.1063/5.0132123.1
research Chapter 16. Ethics in organizations
Organizations must prioritize ethical behavior, guided by leadership, to build trust and competitiveness.
research Identificación de la variación molecular y genética subyacente a las enfermedades de la piel
Non-coding RNAs may be key in diagnosing and treating rare skin disorders.
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 Hair and scalp disease detection using deep learning
Deep learning can effectively detect hair and scalp diseases early.
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 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 Methods of Transfer Learning for Multiclass Hair Disease Categorization
Transfer learning with three neural network architectures accurately classifies hair diseases.
research Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images
Deep learning can improve non-invasive alopecia diagnosis using hair images.
research Hair and Scalp Disease Detection using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed 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 42063 Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images
The model can effectively identify good quality skin images but needs more testing for real-world use.
research Hair Tone Estimation at Roots via Imaging Device with Embedded Deep Learning
The device accurately estimates natural hair color at the roots in real time.
research Revolutionizing Hair Fall Analysis: The Advanced Precipitation U-Net Model
The Advanced Precipitation U-Net Model improves early hair fall detection with 92% accuracy.
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 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 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 Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
A new model can predict drug-disease links well, helping drug research.
research A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Better datasets and methods are needed for reliable vitiligo detection using deep learning.
research Investigating Different Deep learning Models for Classification of Folliculitis
GoogLeNet is the best model for identifying folliculitis.
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.