Skin Diseases Prediction using Deep Learning Framework
Padmavathi S1, Mathu Mithaa E2, Kiruthika T3, Ruba M4
1Padmavathi.S,Department of Computer Science and Engineering, Sri Krishna college of Technology, Coimbatore, India.
2Mathu Mithaa. E, Department of Computer Science and Engineering, Sri Krishna college of Technology, Coimbatore, India.
3Kiruthika.T, Department of Computer Science and Engineering, Sri Krishna college of Technology, Coimbatore, India.
4Ruba. M, Department of Computer Science and Engineering, Sri Krishna college of Technology, Coimbatore, India.
Manuscript received on March 12, 2020. | Revised Manuscript received on March 26, 2020. | Manuscript published on March 30, 2020. | PP: 4781-4784 | Volume-8 Issue-6, March 2020. | Retrieval Number: F9038038620/2020©BEIESP | DOI: 10.35940/ijrte.F9038.038620
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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: Dermatological diseases are found to induce a serious impact on the health of millions of people as everyone is affected by almost all types of skin disorders every year. Since the human analysis of such diseases takes some time and effort, and current methods are only used to analyse singular types of skin diseases, there is a need for a more high-level computer-aided expertise in the analysis and diagnosis of multi-type skin diseases. This paper proposes an approach to use computer-aided techniques in deep learning neural networks such as Convolutional neural networks (CNN) and Residual Neural Networks (ResNet) to predict skin diseases real-time and thus provides more accuracy than other neural networks.
Keywords: Deep Learning, Skin disease, Convolutional Neural Network, Residual Neural Network.
Scope of the Article: Deep Learning.