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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, 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 GoogLeNet outperformed other CNN models in accurately identifying the type of folliculitis.

This review discusses the persistence of post-acute COVID-19 symptoms, referred to as "long COVID," impacting multiple organs and impairing quality of life but reports no new clinical results.

This review summarizes findings on hair loss caused by COVID-19, noting that Telogen Effluvium frequently occurs in female patients 63 days post-infection, often in those with mild to moderate cases and certain comorbidities.