Efficacy of a new combination of a microemulsion and oral supplementation for androgenetic alopecia: an interventional multicentric study

D’Ovidio, Francesco Domenico; Magri, Francesca; Villa, L; Leoncini, Pier Paolo; Caro, Gemma; Mellai, Marta; Fraia, Marco Di; Chieco, Paolo; Hushcha, Yekatsiaryna; Rossi, Alfredo

doi:10.23736/s2784-8671.21.07094-8

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2023

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“…Researchers proposed that the automated evaluation of hair density using YOLOv4 be reviewed using a Deep Neural Network [6]. As a result, this research was carried out to assess the accuracy of several deep-learning-based HDM algorithms as well as the possibility of automating HDM [7]. Using cuttingedge deep-learning-based object detection technology, the feasibility of automated HDM was explored (specifically, EfficientDet, YOLOv4, and DetectoRS) [8].…”

Section: Literature Reviewmentioning

confidence: 99%

Scalp disease analysis using deep learning models

Krishnamoorthy,

Jayanthi,

Kumaravel

et al. 2023

ACE

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Alopecia areata, folliculitis, hair loss, and dandruff are common scalp hair disorders caused by nutritional imbalances, stress, and pollution in the environment. Specialized treatments, such as hair physiotherapy, have arisen to address this issue. This paper pro-poses a deep learning model that can analysis the scalp diseases with high accuracy, using this model we can predict the scalp diseases which makes easier for them to treat the dis-ease with good treatment using the mobile phones we can predict the diseases simply us-ing its image. This paper deals with an advanced classification model which predicts scalp disease. More than 1100 images are used as training dataset, CNN is used for the classifi-cation and the high accuracy is achieved using VGG16, VGG19 and MobileNetV2.

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Section: Literature Reviewmentioning

confidence: 99%