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- Harnessing Deep Learning for Scalp and Hair Disease Classification: A Comparative Study of Convolutional Neural Networks Architectures
- Hair Disease Classification Using Convolutional Neural Network (CNN) Algorithm with VGG-16 Architecture
- Convolutional Neural Networks for Non-Invasive Diagnosis of Androgenetic Alopecia using Dermoscopic Hair Images
- 42063 Image Quality Assessment using Convolutional Neural Network in Clinical Skin Images
- Identification of Drug-Disease Associations Using Information of Molecular Structures and Clinical Symptoms via Deep Convolutional Neural Network
- Detection of Meibomian Gland Dysfunction by in vivo Confocal Microscopy Based on Deep Convolutional Neural Network
- In Vivo Confocal Microscopy for Automated Detection of Meibomian Gland Dysfunction: A Study Based on Deep Convolutional Neural Networks
- Artificial neural networks algorithms for prediction of human hair loss related autoimmune disorder problem
- Generative adversarial networks for anonymous acneic face dataset generation
- Deep Clustering via Center-Oriented Margin Free-Triplet Loss for Skin Lesion Detection in Highly Imbalanced Datasets
- Prediction of Alopecia Areata using CNN
- Trichoscopy of Alopecia Areata: Hair Loss Feature Extraction and Computation Using Grid Line Selection and Eigenvalue
- Ensemble of pre-learned deep learning model and an optimized LSTM for Alopecia Areata classification
- Hair & Scalp Disease Detection Using Machine Learning & Image Processing
- Hair Tone Estimation at Roots via Imaging Device with Embedded Deep Learning
- Quantitative analysis and development of alopecia areata classification frameworks
- Predicting Hair Loss with AI: A Deep Learning Framework Combining Genetic and Scalp Health Data
- Diagnosis of Scalp Disorders using Machine Learning and Deep Learning Approach -- A Review
- Automating Hair Loss Labels for Universally Scoring Alopecia From Images
- Hair and scalp disease detection using deep learning
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Classification of Visually Similar Scalp Diseases using Deep Learning: A Hybrid CNN-VIT Approach with Cross-Attention Fusion
- Vitadetect : Vitamin Deficiency Detection
- DETECTION OF HAIR FALL AND SCALP DISORDERS THROUGH ML AND IMAGE PROCESSING
- Automated early detection of androgenetic alopecia using deep learning on trichoscopic images from a Korean cohort: a retrospective model development and validation study
- Methods of Transfer Learning for Multiclass Hair Disease Categorization
- Investigating Different Deep learning Models for Classification of Folliculitis
- BaldPredict: AI-Driven Prediction Model for Alopecia Areata
- Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration