Convolutional Neural Network Model for Predicting Skin Based Diseases and Evaluation of Risk Assessment
S.A.K. Jainulabudeen1, S. Murugavalli2, H. Shalma3 

1S.A.K. Jainulabudeen, Computer Science and Engineering, Panimalar Engineering College, Chennai, (Tamil Nadu) India.
2Dr. S. Murugavalli, Computer Science and Engineering, Panimalar Engineering College, Chennai, (Tamil Nadu) India.
3H. Shalma, Computer Science and Engineering, Saveetha School of Engineering, Chennai, (Tamil Nadu) India.

Manuscript received on 07 March 2019 | Revised Manuscript received on 14 March 2019 | Manuscript published on 30 July 2019 | PP: 989-993 | Volume-8 Issue-2, July 2019 | Retrieval Number: B1762078219/19©BEIESP | DOI: 10.35940/ijrte.B1762.078219
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Abstract: In today’s dynamic lifestyle, with people not prioritizing hygiene as an essential entity, they tend to get more easily prone to skin diseases. As a result, it has become highly significant to devise an automated mechanism which helps users to predict a disease using simple methodologies such as input in the form of images. In our case we have used Convolutional Neural Networks to diagnose the same. The disease prediction system uses simple methodologies including taking user input in the form of images, which aids in providing more accurate results. As the concept used is Convolutional Neural Network (CNN), the system not just analyses the input and predicts the disease based on it, but also guesses the nearest possible result based on its feature and adds that record to that class of disease for future predictions. Over a longer period of time, this majorly helps in preventing hazardous skin diseases from causing widespread damage, as spreading of skin diseases is highly rapid and difficult to control, the ultimate aim through this project is to overcome the above limitations and help create awareness amongst people on skin diseases, which happens to be a growing concern in the near future.
Index Terms: Skin Diseases, Convolutional Neural Networks, Datasets, Prediction, Geo Location.

Scope of the Article: Performance Evaluation of Networks