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.
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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 A Comprehensive Survey on Vitiligo Detection Using Deep Learning
Better datasets and methods are needed for reliable vitiligo detection using deep learning.
research Generative adversarial networks for anonymous acneic face dataset generation
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
research Hair cluster detection model based on dermoscopic images
The new model detects hair clusters more accurately and efficiently, helping with early hair loss treatment and diagnosis.
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 Weakly-Supervised Hair SEM Microscope Image Segmentation Using a Priori Structure Information
The method improves hair image segmentation accuracy while reducing annotation costs.
research MobiMed: Framework for Rapid Application Development of Medical Mobile Apps
The framework helps develop medical apps on mobile devices to reduce reliance on desktop computers.
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 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 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 Far-field RF-powered closed-loop bioelectronic system for enhanced wound healing and connected healthcare
A wireless, battery-free system uses Wi-Fi signals to enhance wound healing and enable smart healthcare at home.
research In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
research Advances in Digital Light Processing (DLP) Bioprinting: A Review of Biomaterials and Its Applications, Innovations, Challenges, and Future Perspectives
DLP bioprinting shows promise for medical uses, but needs more material options and strength improvements.
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 An Early Hair Loss Detection and Prediction Method Based on Machine Learning
Machine learning can accurately predict hair loss early, improving treatment options.
research Emerging Strategies in Smart Nano-PROTAC for Stimuli-Responsive Protein Degradation and Precision Cancer Therapy
Smart nano-PROTACs improve cancer treatment by targeting proteins more precisely and reducing side effects.
research Nobiletin-loaded composite penetration enhancer vesicles restore the normal miRNA expression and the chief defence antioxidant levels in skin cancer
Nobiletin-loaded vesicles effectively treat skin cancer by restoring normal miRNA and antioxidant levels.
research Self-assembled polymeric nanocarriers for the targeted delivery of retinoic acid to the hair follicle
The new micelle formulation delivers acne treatment more effectively and safely than current gels.
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 MICRO-URZĄDZENIA, BEZPRZEWODOWE KAPSUŁKI, MAPY, STYMULATORY I ROZRUSZNIKI STOSOWANE DO DIAGNOSTYKI I LECZENIA ZABURZEŃ MOTORYKI PRZEWODU POKARMOWEGO
New technologies improve diagnosis and treatment of digestive disorders.
research Remote loading of minoxidil in nano-reservoirs leads to polymorphism and controlled release
research 877 Integration of magnetic tweezers and traction force microscopy for exploring the mechanobiology of keratinocyte cell-cell and cell-matrix anchoring junctions
The conclusion is that a new method combining magnetic tweezers and traction force microscopy may help understand skin cell interactions and diseases.
research Triboelectric Nanogenerators for Therapeutic Electrical Stimulation
Triboelectric nanogenerators can use body movement to power therapeutic treatments, potentially transforming personalized healthcare.
research MultiKano: an automatic cell type annotation tool for single-cell multi-omics data based on Kolmogorov–Arnold network and data augmentation
MultiKano accurately identifies cell types in complex data better than existing methods.
research Leucine-activated nanohybrid biofilm for skin regeneration via improving cell affinity and neovascularization capacity
The biofilm enhances skin healing by promoting cell growth and blood vessel formation.
research A wearable and stretchable dual-wavelength LED device for home care of chronic infected wounds
A new wearable LED device helps heal chronic infected wounds at home.
research Hydrogel Lego based on rehydrated laminate assembly for standardized production and customized integration of multifunctional hydrogel sensors
New hydrogel sensors can be quickly made and customized for wearable devices.
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.