ArticleJournal of imaging2022
Hair Follicle Classification and Hair Loss Severity Estimation Using Mask R-CNN.
Article in Journal of imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed.
- A Two-Level Illumination Correction Network for Digital Meter Reading Recognition in Non-Uniform Low-Light Conditions.Journal of imaging · 2026Article
- Relevance of topical application of minoxidil laden nano-lipid for hair growth enhancement.Saudi pharmaceutical journal : SPJ : the official publication of the Saudi Pharmaceutical Society · 2025Article
- Transforming Aesthetic Dermatology: The Role of Artificial Intelligence in Skin Health.Dermatology and therapy · 2025Review
- Multi-omics and AI-driven advances in miRNA-mediated hair follicle regulation in cashmere goats.Frontiers in veterinary science · 2025Review
- Intelligent detection for Polycystic Ovary Syndrome (PCOS): Taxonomy, datasets and detection tools.Computational and structural biotechnology journal · 2025Review
- Enhancing assisted diagnostic accuracy in scalp psoriasis: A Multi-Network Fusion Object Detection Framework for dermoscopic pattern diagnosis.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Leveraging deep neural networks to uncover unprecedented levels of precision in the diagnosis of hair and scalp disorders.Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) · 2024Article
- Vision Transformer Customized for Environment Detection and Collision Prediction to Assist the Visually Impaired.Journal of imaging · 2023Article
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Early and accurate detection of scalp hair loss is imperative to provide timely and effective treatment plans to halt further progression and save medical costs. Many techniques have been developed leveraging deep learning to automate the hair loss detection process. However, the accuracy and robustness of assessing hair loss severity still remain a challenge and barrier for transitioning such a technique into practice. The presented work proposes an efficient and accurate algorithm to classify hair follicles and estimate hair loss severity, which was implemented and validated using a multitask deep learning method via a Mask R-CNN framework. A microscopic image of the scalp was resized, augmented, then processed through pre-trained ResNet models for feature extraction. The key features considered in this study concerning hair loss severity include the number of hair follicles, the thickness of the hair, and the number of hairs in each hair follicle. Based on these key features, labeling of hair follicles (
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Registered trials
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