Dermatological Disease Classification utilizing Image Processing and Neural Networks
Shikha Malik1, V.V Dixit2

1Nur Whakidah*, Department of Informatics Engineering, Semarang University, Semarang, Indonesia.
2Siti Asmiatun, Department of Informatics Engineering, Semarang University, Semarang, Indonesia.
3Astrid Novita Putri, Department of Informatics Engineering, Semarang University, Semarang, Indonesia. 

Manuscript received on December 02, 2020. | Revised Manuscript received on December 05, 2020. | Manuscript published on December 30, 2020. | PP: 127-131 | Volume-10 Issue-2, December 2020. | Retrieval Number: 100.1/ijeat.B20531210220 | DOI: 10.35940/ijeat.B2053.1210220
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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: Skin diseases are a frequent problem among all age groups. Application of Machine Learning (ML) is exceedingly suitable for skin diseases identification as it has large clinical image database that can be used to train models and interpret diagnosis for better patient outcomes. Researchers have used various image processing techniques and classification methods. Color and Texture based features are most commonly used for image analysis and Convolutional Neural Network (CNN) has become current standard practice in classifying skin disorders. This paper presents a thorough survey of image processing techniques and classifiers for skin diseases detection. 
Keywords: Image Processing, Machine Learning, Neural Networks.