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This study found that a combined machine learning model outperformed individual networks in diagnosing scalp conditions using visual data, enhancing prediction accuracy and early detection, particularly in settings with limited resources.

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.

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

This study observed that during the postnatal development of gray short-tailed opossums, skin transitions from facilitating gas exchange through a dense capillary network at birth to supporting thermoregulation, with significant changes in skin thickness and structure occurring by 35 days old.