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- AB-MTEDeep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
- Big Self-Supervised Models Advance Medical Image Classification
- Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
- Bias Assessment and Data Drift Detection in Medical Image Analysis: A Survey
- Optimized polycystic ovarian disease prognosis and classification using AI based computational approaches on multi-modality data
- Generative adversarial networks for anonymous acneic face dataset generation
- Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
- Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
- Hair analysis based on medical history and spatial-temporal data
- BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features
- Optimized VGG19 Architecture for Precise and Efficient Multi-Class Hair Disease Classification
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
- Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
- The Hair Cell Analysis Toolbox: A machine learning-based whole cochlea analysis pipeline
- Closing the AI generalization gap by adjusting for dermatology condition distribution differences across clinical settings
- Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia
- Optimizing skin disease diagnosis: harnessing online community data with contrastive learning and clustering techniques
- Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
- Automated analysis of scanning electron microscopic images for assessment of hair surface damage
- Classification Framework for Healthy Hairs and Alopecia Areata: A Machine Learning (ML) Approach
- Quantitative analysis and development of alopecia areata classification frameworks
- Crowdsourcing Dermatology Images with Google Search Ads: Creating a Real-World Skin Condition Dataset
- Hair cluster detection model based on dermoscopic images
- How different is human hair? A critical appraisal of the reported differences in global hair fibre characteristics and properties towards defining a more relevant framework for hair type classification
- Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
- Re-identification of anonymised MRI head images with publicly available software: investigation of the current risk to patient privacy
- Hair analysis based on medical history and spatial-temporal data
- Relationship between Illness Behavior and Hair Loss Pattern According to the Basic and Specific (BASP) Classification
- Deep Learning Approaches for Hair Disease Classification: A Comparative Analysis of MobileNetV2 and VGG19 Architectures
- Research on hair removal algorithm of dermatoscopic images based on maximum variance fuzzy clustering and optimization Criminisi algorithm