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Research 15
- 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
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders
- HairLossMultinet: A Multi Scale Feature Fusion Method Using Deep Learning Approach
- Deep Learning-Powered Hair Disease Diagnosis: A ResNet50 Approach for Scalable and Accurate Classification
- BaldGraphFormer: An Explainable Deep Learning Framework for Early Baldness Prediction by Integrating Swin Transformer, Graph Attention Networks and Clinical Features
- Weakly-Supervised Hair SEM Microscope Image Segmentation Using a Priori Structure Information
- A machine learning approach for non-invasive PCOS diagnosis from ultrasound and clinical features
- Scalp Disorder Imaging: How Deep Learning and Explainable Artificial Intelligence are Revolutionizing Diagnosis and Treatment
- Exossomos na Dermatologia: revisão de sua ação em doenças cutâneas e rejuvenescimento
- Skin image analysis for detection and quantitative assessment of dermatitis, vitiligo and alopecia areata lesions: a systematic literature review
- A Study on the Development of a Web Platform for Scalp Diagnosis Using EfficientNet
- Trichoscopy and Computational Models for Hair and Scalp Disorders: Image Analysis, Quantification, and Clinical Integration
- HairSentinel: a time-aware anomaly detection framework for forecasting hairfall trends using temporal fusion transformers