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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.

In this study, a novel GAN-based image translation method focusing on regions of interest showed improved predictive performance for post-hair transplant images compared to existing methods, using an ensemble approach to enhance robustness and detection accuracy.

This study found that iodine deficiency, iodine excess, or the presence of antithyroid substances affected growth and behavior in Wistar rats, leading to various symptoms such as lethargy and alopecia.