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In this study, VGG19 slightly outperformed MobileNetV2 in hair disease classification accuracy, achieving 98% compared to MobileNetV2's 97%. However, MobileNetV2 was faster and more computationally efficient, making it suitable for resource-limited settings.

In this study, a deep learning model using an optimized VGG19 architecture achieved a high classification accuracy of 98.64% for detecting ten hair disease classes from a balanced dataset, indicating its potential for reliable use in mobile diagnostics for clinical and remote applications.

This study demonstrated that a CNN-based model using VGG-16 architecture achieved a 94.5% accuracy in classifying ten types of hair diseases, implying a promising tool for aiding health professionals in diagnosing hair conditions accurately.

This study found that a VGG-SVM model using machine learning techniques achieved 98.31% accuracy in distinguishing alopecia areata from healthy hair based on image datasets.

This review examines the neuroendocrine mechanisms underlying seasonal changes in mammals and highlights areas needing further research, reporting no new experimental results.

This study found that significant hair loss in about one-third of patients after vertical gastroplasty was reversed by zinc supplementation.

This study evaluates a framework using neural networks and machine learning techniques to classify and detect Alopecia Areata from hair images, aiming for accurate differentiation between healthy hair and the condition.

This study found that minoxidil and ranolazine, individually and in combination, reduced cellular invasiveness in hypoxic triple-negative breast cancer cell lines, suggesting potential anti-metastatic effects at clinically relevant doses.

This study developed a deep learning model using the VGG architecture to predict hair disorders and provide tailored therapeutic suggestions, showing reliable recognition of conditions like dandruff, fungal infections, and alopecia by analyzing images of hair and scalp.

This study found that the vitamin D receptor plays a crucial role in hair follicle development in cashmere goats by regulating signaling pathways in dermal papilla cells.

This study evaluated different convolutional neural network architectures for diagnosing scalp and hair diseases, and found that VGG16 and VGG19 consistently outperformed other models in accuracy, demonstrating their effectiveness and reliability in this medical application.

In this study, a modified Xception deep learning model achieved a 92% accuracy rate in diagnosing hair and scalp disorders, significantly outperforming other models, suggesting AI could improve dermatological diagnostics' accuracy and accessibility.

This study introduced a deep learning framework combining multiple convolutional neural networks to detect scalp and hair disorders and classify hair fall stages, reporting higher precision and robustness in detection and classification compared to individual CNN models.

HairLossMultinet accurately classifies hair damage with 98% accuracy but needs a more diverse dataset for broader use.

This study on male rats found that finasteride treatment led to the differential expression of several genes in the hypothalamus and hippocampus, hinting at potential links to observed side effects like depression, anxiety, and cognitive disturbances.

This study aims to develop an automatic machine learning-based method using the VGG-19 model to accurately classify various hair and scalp diseases.

This study found that applying transfer learning with CNN architectures like AlexNet, VGG16, and ResNet50 achieved 99% accuracy in classifying multiclass hair disorders, suggesting a potential technological aid for dermatologists in diagnosing and treating hair conditions.

This study found that GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.

In this study, the researchers used machine learning techniques on an image dataset to diagnose Alopecia Areata, achieving a maximum accuracy of 98.3%.

This study found that bioelectric and biochemical signaling mechanisms coordinate collective cell movement during chicken feather bud morphogenesis, suggesting a potential new angle for research in skin development and wound healing.

Several companies launched new hair care ingredients in 2011 to improve conditioning, color retention, combability, and heat protection.

This study proposes a deep learning model using CNN with VGG16, VGG19, and MobileNetV2 architectures, achieving high accuracy in classifying scalp diseases from images, potentially facilitating diagnosis and treatment via mobile devices.

This study used machine learning models to analyze PCOS symptoms for early diagnosis, finding Support Vector Machine and VGG16 algorithms achieved high accuracy rates of 94.44% and 98.29% respectively.

This study found that the restoration of CD103⁺γδ T cells in humans is associated with sustained remission in inflammatory bowel disease, suggesting a conserved role for these cells in limiting disease progression.

This study examined the potential benefits of incorporating cannabis-derived compounds like cannabidiol and seed oil into skincare products, finding they may improve skin health and address inflammation and dermatological issues, thus supporting their role in developing effective cosmeceuticals.

This review examines the prevalence and symptoms of polycystic ovary syndrome (PCOS) across different ages, highlighting its varied dermatological and reproductive implications, but reports no new research findings.

This article reviews the diagnostic challenges of polycystic ovary syndrome in adolescents, discussing criteria, symptoms overlap with normal puberty, and treatment strategies, but it reports no new clinical findings.

In this study on mice with severe thermal skin burns, researchers found that administering 4-aminopyridine (4-AP) helped reduce inflammation and apoptosis, promoted angiogenesis, and accelerated wound healing by enhancing specific cellular behaviors and signaling pathways.

This study found that mesenchymal stem cells from chronically inflamed human livers maintain their key cellular characteristics, supporting their potential use in developing liver cell therapies.

This study found that among women with PCOS, the most common clinical symptoms were menstrual dysfunction, infertility, acne, and hirsutism, with the non-androgenic phenotype being the most frequently identified.