10 citations
,
January 2016 in “ACG Case Reports Journal” Long-term Cape Aloe use causes harmless colon pigmentation that can help detect polyps.
July 2007 in “Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature” The BASP classification is a detailed system for categorizing hair loss in both men and women, but it may be complex for beginners and not fully suitable for grading female hair loss.
1 citations
,
December 2019 in “Dermatologic Therapy” Only anti-androgenic drugs likely halt AGA progression.
5 citations
,
October 2023 in “International Journal on Recent and Innovation Trends in Computing and Communication” The method accurately detects and classifies scalp diseases, including alopecia areata, with 89.3% accuracy.
The model accurately diagnoses hair diseases with 95% accuracy using deep learning.
The model accurately identifies hair diseases using deep learning.
7 citations
,
October 2023 in “Journal of Intelligent & Fuzzy Systems” The new model improves Alopecia Areata classification accuracy to 93.1%.
Nonlinear artificial neural networks are better at identifying different types of animal hair than linear ones.
January 2025 in “Communications in computer and information science” HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.
4 citations
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October 2022 in “Journal of Imaging” An intelligent system can classify hair follicles and measure hair loss severity with reasonable accuracy.
May 2023 in “Indian journal of science and technology” The new deep learning system can accurately recognize hair loss conditions with a 95.11% success rate.
January 2021 in “arXiv (Cornell University)” Self-supervised learning improves medical image classification accuracy.
Transfer learning with three neural network architectures accurately classifies hair diseases.
2 citations
,
January 2024 in “IEEE Access” AlopeciaDet accurately detects Alopecia Areata early using advanced image analysis.
The optimized VGG19 model accurately classifies hair diseases with 98.64% accuracy.
2 citations
,
January 2024 in “Journal of Emerging Investigators” A new algorithm effectively classifies Alopecia Areata, aiding early detection and treatment.
1 citations
,
May 2025 in “Journal of Digital Information Management” VGG16 and VGG19 are the most accurate for classifying scalp and hair diseases.
January 2026 in “Pattern Recognition” The new method improves accuracy in segmenting scalp tissue layers.
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.
February 2026 in “International journal of intelligent engineering and systems” The new method improves hair segmentation in skin images, helping detect skin cancer more accurately.
10 citations
,
January 2012 in “International Journal of Trichology” PRP helps hair growth in common hair loss disorder.
April 2021 in “Journal of Investigative Dermatology” A deep learning model was developed to help diagnose trichothiodystrophy by analyzing hair patterns.
19 citations
,
October 2024 in “BMC Medical Informatics and Decision Making” AI can improve early diagnosis and classification of PCOS, aiding in prevention of related health issues.
October 2023 in “Sinkron” The system can accurately classify hair diseases with 94.5% accuracy using a CNN.
5 citations
,
June 2023 in “Engineering Technology & Applied Science Research” The AI model accurately classifies Alopecia Areata with 96.94% accuracy.
January 2022 in “Journal of Pharmaceutical Negative Results” The VGG-SVM method accurately identifies and classifies stages of Alopecia Areata and other hair loss conditions.
April 2019 in “Journal of Investigative Dermatology” The search scheme SMRI is faster and more secure for retrieving encrypted data from the cloud.
2 citations
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November 2018 in “Modern Applied Science” The method accurately detects and removes hair from skin images to improve melanoma diagnosis.
The model accurately classifies hair conditions with 97% accuracy.
December 2021 in “Acta dermato-venereologica” A deep learning model accurately predicts male hair loss types using scalp images.