Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/2068
Title: CURETO: Skin Diseases Detection Using Image Processing And CNN
Authors: Karunanayake, R. K. M. S. K
Dananjaya, W. G. M
Peiris, M. S. Y
Gunatileka, B. R. I. S
Lokuliyana, S
Kuruppu, A
Keywords: CURETO
Skin Diseases
Detection
CNN
Image Processing
Issue Date: 17-Nov-2020
Publisher: IEEE
Citation: R. K. M. S. K Karunanayake, W. G. M. Dananjaya, M. S. Y Peiris, B. R. I. S. Gunatileka, S. Lokuliyana and A. Kuruppu, "CURETO: Skin Diseases Detection Using Image Processing And CNN," 2020 14th International Conference on Innovations in Information Technology (IIT), 2020, pp. 1-6, doi: 10.1109/IIT50501.2020.9299041.
Series/Report no.: 2020 14th International Conference on Innovations in Information Technology (IIT);Pages 1-6
Abstract: Busy lifestyles these days have led people to forget to drink water regularly which results in inadequate hydration and oily skin, oily skin has become one of the main factors for Acne vulgaris. Acne vulgaris, particularly on the face, greatly affects a person's social, mental wellbeing and personal satisfaction for teens. Besides the fact that acne is well known as an inflammatory disorder, it was reported to have caused serious long-term consequences such as depression, scarring, mental illness, including pain and suicide. In this research work, a smartphone-based expert system namely “Cureto” is implemented using a hybrid approach i.e. using deep convolutional neural network (CNN) and natural language processing (NLP). The proposed work is designed, implemented and tested to classify Acne density, skin sensitivity and to identify the specific acne subtypes namely whiteheads, blackheads, papules, pustules, nodules and cysts. The proposed work not only classifies Acne Vulgaris but also recommends appropriate treatments based on their classification, severity and other demographic factors such as age, gender, etc. The results obtained show that for Acne type classification the accuracy ranges from 90%-95% and for Skin Sensitivity and Acne density the accuracy ranges from 93%-96%.
URI: http://rda.sliit.lk/handle/123456789/2068
ISSN: 2325-5498
Appears in Collections:Department of Computer Systems Engineering-Scopes
Research Papers - Dept of Computer Systems Engineering
Research Papers - IEEE
Research Papers - SLIIT Staff Publications

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