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.
47 citations
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July 2023 in “Nature Genetics” 
January 2021 in “Lecture notes in networks and systems” Deep learning can accurately detect Alopecia Areata with up to 98.3% accuracy.
74 citations
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January 2020 in “IEEE Access” ScalpEye accurately diagnoses scalp issues like dandruff and hair loss.
4 citations
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May 2024 in “INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT” AI can accurately diagnose hair and scalp conditions and suggest treatments.
1 citations
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May 2025 in “Journal of Digital Information Management” VGG16 and VGG19 are the most accurate for classifying scalp and hair diseases.
1 citations
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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
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January 2023 in “IEEE access” Deep learning helps detect skin conditions and is advancing dermatology diagnosis and treatment.
Machine learning can accurately predict hair loss early, improving treatment options.
