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 |
Files in This Item:
File | Description | Size | Format | |
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CURETO_Skin_Diseases_Detection_Using_Image_Processing_And_CNN.pdf Until 2050-12-31 | 219.62 kB | Adobe PDF | View/Open Request a copy |
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