Please use this identifier to cite or link to this item: https://rda.sliit.lk/handle/123456789/3320
Title: Face Skin Disease Detection and Community based Doctor Recommendation System
Authors: Udara, M.A.A.
Wimalki Dilshani, D.G.
Mahalekam, M.S.W.
Wickramaarachchi, V.Y.
Krishara, J
Wijendra, D
Keywords: Doctor Recommendation System
Community based
Disease Detection
Face Skin Disease
Issue Date: 9-Dec-2022
Publisher: IEEE
Citation: M. A. A. Udara, D. G. Wimalki Dilshani, M. S. W. Mahalekam, V. Y. Wickramaarachchi, J. Krishara and D. Wijendra, "Face Skin Disease Detection and Community based Doctor Recommendation System," 2022 4th International Conference on Advancements in Computing (ICAC), Colombo, Sri Lanka, 2022, pp. 393-398, doi: 10.1109/ICAC57685.2022.10025338.
Series/Report no.: 2022 4th International Conference on Advancements in Computing (ICAC);
Abstract: In our country, skin diseases are more common than other diseases because of the climate. Skin diseases are occurring almost on all groups of ages among people. It is one of the most common types of diseases where some can be painful, and some can cause fatal to human life. The delay of the disease detection, difficulties of identify the infected area, Ignorance of the spread of the disease and treatments may threat to the patient’s life. Most of the time this process is performed manually which can lead to human errors and takes days for providing the results. This paper reports a smart solution that assists the patients by detecting the disease, identify the current infected area of the disease, recommend best doctors, provide community-based prevention guidelines, and predict the future risk. Also due to this economic crisis, we suggest that it’s much easier if the patient can do these skin check-ups systematically to continuously monitor and detect skin disease to get proper medical attention. As treatment procedures can be different from each doctor and impact will be different, we are working on community-based platform where we can get patients’ reviews about doctors and preventive guidelines. Depending on the performance evaluations, the results obtained from the proposed method for disease identifications are in the range of 90% - 95% of accuracy.
URI: https://rda.sliit.lk/handle/123456789/3320
ISSN: 979-8-3503-9809-0
Appears in Collections:4th International Conference on Advancements in Computing (ICAC) | 2022
Department of Computer Science and Software Engineering
Research Papers - Dept of Computer Science and Software Engineering
Research Papers - IEEE
Research Publications -Dept of Information Technology

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