Search

everythingexperts & clinicslearnfaqsresearchcommunity

for

Search:↓

This study presents a new hair removal algorithm for dermatoscopic images of skin lesions that improves hair detection accuracy by 2–7% and hair repair accuracy by 2–5% on average, using advanced techniques like maximum variance fuzzy clustering, Criminisi priorities, and the ant colony algorithm.

This review examines advancements in deep learning methods for detecting dermatological conditions from dermoscopic images, summarizing available datasets and suggesting future research directions, but reports no new results.

In this study, researchers developed a method to create a synthetic dataset of facial acne images using generative techniques, achieving 97.6% classification accuracy with InceptionResNetv2, which helps overcome privacy concerns in biomedical applications by using anonymized data.

This research reported that using J48 algorithms with bagging improves prediction accuracy of hair health through machine learning by analyzing factors like spatial-temporal images, gender, and age, achieving a real-time performance of 89.5%.

The authors of this study developed a novel CNN architecture aimed at improving detection of Alopecia Areata through image-based datasets, achieving a top accuracy of 98% compared to four other machine learning models.

This study found that their proposed method for facial image restoration using Denoising Diffusion Probabilistic Models produced higher-quality results compared to traditional methods, particularly improving face recognition accuracy with different types of masks.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.

This review examines the use of computer vision techniques, specifically deep learning architectures and image processing algorithms, for detecting and assessing skin conditions like vitiligo and dermatitis, and highlights the need for disease-specific datasets to improve automated diagnostic tools in dermatology.

This study introduces ALUDWIG, an automated tool for assessing female androgenic alopecia severity from smartphone images, which may offer a more consistent alternative to current scoring methods like the Ludwig scale.

This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

This study evaluated a new AI-linked imaging device for grading dandruff severity, finding high correlation with expert assessments and suggesting its potential application in other skincare areas due to its versatility and ease of use.

This study demonstrates that an AI-based grading framework using a novel area ratio metric improves the accuracy and consistency of male pattern hair loss classification, especially in advanced grades, compared to traditional methods.

This paper introduces an algorithm for using smart device-mounted microscopes to analyze scalp images and diagnose hair loss by extracting specific hair loss features.

This study developed a support vector machine model using trichoscopic patterns to accurately classify androgenic alopecia severity, with an accuracy of 94.3% in training and 90.0% in test datasets.

In this study, machine learning-based computer-aided diagnosis significantly improved accuracy in diagnosing alopecia areata compared to traditional visual methods, achieving up to 91.9% accuracy using different classifiers like CNN, SVM, and random forest models.

In this study, researchers developed a deep learning model that efficiently classifies five degrees of harm with high accuracy, achieving up to 98% precision, recall, and F1-score across various harm levels, indicating strong potential for practical application in automated harm evaluation.

This study found that applying transfer learning with CNN architectures like AlexNet, VGG16, and ResNet50 achieved 99% accuracy in classifying multiclass hair disorders, suggesting a potential technological aid for dermatologists in diagnosing and treating hair conditions.

This study reported that a new deep learning algorithm using Mask R-CNN improved hair follicle classification accuracy by 4 to 15%, suggesting potential clinical application for enhanced hair loss diagnosis.

In this study, researchers used Google Search ads to gather an open access dataset of 10,408 dermatological images from over 5,000 U.S. internet users, enhancing the diversity and representativeness of skin condition images available for research and artificial intelligence development.

This study developed a model for detecting sparse hair clusters using enhanced object detection neural networks and medical images, which accurately identifies and counts sparse hair clusters with greater accuracy and efficiency than existing methods.

This study reviews methods to ensure the reliability of machine learning models in medical imaging, focusing on bias detection, data drift assessment, and accuracy estimation without ground truth labels to enhance integration into clinical settings.

In this study, improved hidden Markov algorithms based on Bayesian methods enhanced the resolution and segmentation accuracy of low-dose CT images significantly more than naive Bayesian methods.

In this study, a novel GAN-based image translation method focusing on regions of interest showed improved predictive performance for post-hair transplant images compared to existing methods, using an ensemble approach to enhance robustness and detection accuracy.

This study introduced a novel, weakly supervised method for segmenting hair in Scanning Electron Microscope images using simple image-level annotations, achieving over 30% improvement in mean Hausdorff Distance compared to Unet and SAM, while enhancing interpretability and refinement.

In this study, the researchers reported that their novel algorithm for hair restoration in dermoscopy images achieved high accuracy and texture preservation, outperforming other techniques in diagnostic accuracy and texture quality measures.

This study presents a new approach to automatically remove hair artifacts from dermoscopic images, which reportedly performed well compared to existing methods like DullRazor using the PH2 datasets.

This study reported that dutasteride, identified through a drug repurposing strategy, showed the potential to reduce neuroinflammation and cognitive impairment in LPS-stimulated models.

This study reports that the ScalpEye system accurately diagnosed dandruff, folliculitis, hair loss, and oily hair with a precision range of 97.41% to 99.09%.

This study found a significant association between montelukast and neuropsychiatric adverse events such as suicidal ideation and depression, suggesting that the drug's interaction with specific genes may contribute to these effects.