Quantitative analysis and development of alopecia areata classification frameworks
January 2024
in “
Journal of Emerging Investigators
”
Research cited in this study
7 / 7 resultsresearch Quantitative analysis and development of alopecia areata classification frameworks
research AB-MTE Deep Classifier Trained with AAGAN for the Identification and Classification of Alopecia Areata
The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
research Attention Balanced Multi-Dimension Multi-Task Deep Learning for Alopecia Recognition
The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
research Hair and Scalp Disease Detection Using Machine Learning and Image Processing
The machine learning model accurately detected hair loss and scalp diseases using processed images.
research Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN
An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.
research An Analysis of Alopecia Areata Classification Framework for Human Hair Loss Based on VGG-SVM Approach
The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
research ScalpEye: A Deep Learning-Based Scalp Hair Inspection and Diagnosis System for Scalp Health
ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.
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