Derma Net: An Automated Skin Lesion Analyzer Using CNN with Adaptive Learning
Santhi H1, Gopichand G2, K.Pavan Koushik3, A.Nithin Krishna4, D. Sai Tharun5
1Santhi H, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (TamilNadu), India.
2Gopichand G, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (TamilNadu), India.
3K. Pavan Koushik, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (TamilNadu), India.
4A.Nithin Krishna, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (TamilNadu), India.
5D. Sai Tharun, School of Computer Science and Engineering, Vellore Institute of Technology, Vellore (TamilNadu), India.
Manuscript received on 08 April 2019 | Revised Manuscript received on 15 April 2019 | Manuscript Published on 26 July 2019 | PP: 513-515 | Volume-8 Issue-6S4 April 2019 | Retrieval Number: F11070486S419/19©BEIESP | DOI: 10.35940/ijitee.F1107.0486S419
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In this paper we are going to develop an automated skin lesion analyzer that can take affected skin lesion image from user and predict or approximate 3 skin diseases with 95% accuracy. To accomplish this goal we are going to use Neural Networks as they are the best data driven models with top most accuracy in all the fields they have been experimented till now. Since Neural Network models also need huge computation power to train the model on the input data and also to predict the output we are going to use a computationally less intensive architecture that can work even on hand held mobiles and embedded systems. To further featuring our model we have added dropout techniques for model regularization and adaptive learning rates to achieve global minima with ease even with the presence of plateaus. At last we will deploy a production level web application to serve users across the world.
Keywords: Embedded Systems, Neural Networks, Global Minima, Plateaus, Adaptive Learning Rates.
Scope of the Article: Computer Science and Its Applications