AIMC Topic: Hair

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Deep learning model for hair artifact removal and Mpox skin lesion analysis and detection.

Scientific reports Jul 1, 2025
Accurate identification of Mpox is essential for timely diagnosis and treatment. However, traditional image-based diagnostic methods often struggle with challenges such as body hair obscuring skin lesions and complicating accurate assessment. To addr...
Humans Dermoscopy Deep Learning Hair Hair Removal Measles Image Processing, Computer-Assisted Skin Artifacts
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Advancing dermoscopy through a synthetic hair benchmark dataset and deep learning-based hair removal.

Journal of biomedical optics Nov 19, 2024
SIGNIFICANCE: Early detection of melanoma is crucial for improving patient outcomes, and dermoscopy is a critical tool for this purpose. However, hair presence in dermoscopic images can obscure important features, complicating the diagnostic process....
Hair Deep Learning Hair Removal Melanoma Image Processing, Computer-Assisted Humans Image Interpretation, Computer-Assisted Skin Neoplasms Neural Networks, Computer Dermoscopy
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Deep Hair Phenomics: Implications in Endocrinology, Development, and Aging.

The Journal of investigative dermatology Sep 3, 2024
Hair quality is an important indicator of health in humans and other animals. Current approaches to assess hair quality are generally nonquantitative or are low throughput owing to technical limitations of splitting hairs. We developed a deep learnin...
Aging Deep Learning Mice Humans Phenotype Animals Hair Phenomics Hair Follicle
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Preventive machine learning models incorporating health checkup data and hair mineral analysis for low bone mass identification.

Scientific reports Aug 13, 2024
Machine learning (ML) models have been increasingly employed to predict osteoporosis. However, the incorporation of hair minerals into ML models remains unexplored. This study aimed to develop ML models for predicting low bone mass (LBM) using health...
ROC Curve Minerals Hair Male Middle Aged Algorithms Bone Density Osteoporosis Female Aged Humans Machine Learning
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An all-optical multidirectional mechano-sensor inspired by biologically mechano-sensitive hair sensilla.

Nature communications Apr 4, 2024
Mechano-sensitive hair-like sensilla (MSHS) have an ingenious and compact three-dimensional structure and have evolved widely in living organisms to perceive multidirectional mechanical signals. Nearly all MSHS are iontronic or electronic, including ...
Sensilla Hair Mechanical Phenomena Animals Robotics Electronics
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Machine learning (ML) techniques as effective methods for evaluating hair and skin assessments: A systematic review.

Proceedings of the Institution of Mechanical Engineers. Part H, Journal of engineering in medicine Dec 29, 2023
Machine Learning (ML) techniques provide the ability to effectively evaluate and analyze human skin and hair assessments. The aim of this study is to systematically review the effectiveness of applying Machine Learning (ML) methods and Artificial Int...
Machine Learning Humans Support Vector Machine Neural Networks, Computer Hair Artificial Intelligence
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Integrated convolutional neural network for skin cancer classification with hair and noise restoration.

Turkish journal of medical sciences Oct 16, 2023
BACKGROUND/AIM: Skin lesions are commonly diagnosed and classified using dermoscopic images. There are many artifacts visible in dermoscopic images, including hair strands, noise, bubbles, blood vessels, poor illumination, and moles. These artifacts ...
Humans Neural Networks, Computer Skin Neoplasms Dermoscopy Deep Learning Artifacts Hair
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A challenge of deep-learning-based object detection for hair follicle dataset.

Journal of cosmetic dermatology Apr 6, 2023
BACKGROUND: Deep-learning object detection has been applied in various industries, including healthcare, to address hair loss.
Hair Hair Follicle Alopecia Male Deep Learning Humans Algorithms Female
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Deep Learning-based Trichoscopic Image Analysis and Quantitative Model for Predicting Basic and Specific Classification in Male Androgenetic Alopecia.

Acta dermato-venereologica Jan 26, 2022
Since the results of basic and specific classification in male androgenetic alopecia are subjective, and trichoscopic data, such as hair density and diameter distribution, are potential quantitative indicators, the aim of this study was to develop a ...
Humans Male Deep Learning Hair Alopecia
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Integrative measurement analysis via machine learning descriptor selection for investigating physical properties of biopolymers in hairs.

Scientific reports Dec 21, 2021
Integrative measurement analysis of complex subjects, such as polymers is a major challenge to obtain comprehensive understanding of the properties. In this study, we describe analytical strategies to extract and selectively associate compositional i...
Biopolymers Cattle Keratins Spectroscopy, Fourier Transform Infrared Cats Physical Phenomena Algorithms Humans Animals Machine Learning Hair Swine Magnetic Resonance Spectroscopy
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