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Scalp disease analysis using deep learning models
- 1 Department of CSE, Kongu Engineering College, Tamilnadu, India
- 2 Department of CSE, Kongu Engineering College, Tamilnadu, India
- 3 Department of CSE, Kongu Engineering College, Tamilnadu, India
- 4 Department of CSE, Kongu Engineering College, Tamilnadu, India
- 5 Department of CSE, Kongu Engineering College, Tamilnadu, India
* Author to whom correspondence should be addressed.
Abstract
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.
Keywords
scalp, diseases, VGG16, VGG19, mobileNetV.
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Cite this article
Krishnamoorthy,N.;Jayanthi,P.;Kumaravel,T.;Sundareshwar,V.A.;Harris,R.S.J. (2023). Scalp disease analysis using deep learning models. Applied and Computational Engineering,2,225-231.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of the 4th International Conference on Computing and Data Science (CONF-CDS 2022)
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