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Review: Digital Dermatology Skin Disease Detection Model using Image Processing

P K Aswathi

Abstract


This paper has focused about the different skin disease detection methods. Skin disease is one of the crucial health problems. Among them the malignant melanomas can be potentially fatal if it is not diagnosed and treated at early stages. According to the World Health Organization about 10,000 deaths are reported every year due to these melanomas. Skin diseases were most common kind of infections occurring in people of all the ages. The costs of dermatologists to monitor every patient are very high. This paper consists various approaches skin disease detection methods. The proposed m tech project proposes a skin disease detection method based on image processing techniques. This method is mobile based and hence easily accessible even in remote areas and it is completely noninvasive to patient’s skin. The patient will provides an image of the infected area of the skin as an input to the prototype. Image processing techniques were performed on this image and the detected disease is displayed at the output. The proposed system will highly beneficial in rural areas where access to dermatologists is limited.

 

Keywords: DCT, DWT, SVD, image processing

 


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References


Yadav N., Narang V.K., Shrivastava U. Skin Diseases Detection Models using Image Processing: A Survey. International Journal of Computer Applications (0975–8887) Volume 137–No.12, March 2016.

Jyothilakshmi K.K, Jeeva J.B. Detection of Malignant Skin Diseases Based on the Lesion Segmentation. International Conference on Communication and Signal Processing, April 3-5, 2014, India

Amarathunga A.A.L.C., Ellawala E.P.W.C., Abeysekara G.N. Expert System for Diagnosis of Skin Diseases. International Journal of Scientific & Technology Research Volume 4, issue 01, January 2015 ISSN 2277-8616

Mittraand A.K., Parekh R. An automated system for recognizing disease conditions of human skin.

Kolkur S., Kalbande D.R. Survey of Texture Based Feature Extraction for Skin Disease Detection. IEEE, 2016.


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