TLDR The model accurately classifies hair conditions with 97% accuracy.
The study "Enhanced Hair Disease Classification Using Deep Learning" demonstrates the effectiveness of a machine-learning model in categorizing various hair conditions, such as Alopecia Areata, Tinea Capitis, and Psoriasis, using a dataset of 8,550 images. The model achieved high performance with accuracy values between 88.04% and 88.89%, recall values from 87.80% to 89.19%, and balanced F1 scores ranging from 88.02% to 88.99%. The overall accuracy was 97%, highlighting the model's reliability and efficacy in diagnosing hair diseases, which can significantly improve patient care and quality of life.
January 2021 in “Lecture notes in networks and systems” Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
1 citations,
March 2024 in “Skin research and technology” A new AI model diagnoses hair and scalp disorders with 92% accuracy, better than previous models.
1 citations,
January 2023 in “IEEE access” Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
June 2023 in “International journal on recent and innovation trends in computing and communication” Combining multiple algorithms predicts hair fall more accurately than using single algorithms.
The method creates realistic, anonymous acne face images for research, achieving 97.6% accuracy in classification.
8 citations,
August 2021 in “Computational and Mathematical Methods in Medicine” Machine learning can accurately identify Alopecia Areata, aiding in early detection and treatment of this hair loss condition.
