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Image Processing for Recognition of Skin Diseases
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 149 - Number 3 |
| Year of Publication: 2016 |
| Authors: Suneel Kumar, Ajit Singh |
10.5120/ijca2016911373
|
Suneel Kumar, Ajit Singh . Image Processing for Recognition of Skin Diseases. International Journal of Computer Applications. 149, 3 ( Sep 2016), 37-40. DOI=10.5120/ijca2016911373
Abstract
It is an extremely bulky process to predict a disease based on the visual diagnosis of cell type with precision or accuracy, especially when multiple features are associated. If we get the information about the dead skin which is not visible by naked eyes well in time then we can easily prevent the further spreading of disease on the other part of body. One of the major problems coming in the medical field is that doctors are not able to detect that infected part which is not visible by naked eyes and therefore they only operate the visible infected part of the skin and this may cause a major problem like cancer or any dangerous disease in the future. Skin cancer classification system is developed and the relationship of the skin cancer image across different type of neural network is established. The collected medical images are feed into the system, and using different image processing schemes image properties are enhanced. Useful information can be extracted from these medical images and pass to the classification system for training and testing using MATLAB image processing toolbox for detection of dead skin.
References
- T.H. Lau and A.A. Jumaily, “Automatically Early Detection of Skin Cancer.” 978-0-7695-3879-2/09 IEEE
- Huiyu Zhou, Xuelong Lu, Gerald Schaefer, M. Emre Celebi, Paul Miller, “Mean shift based gradient vector flow for image segmentation.” Computer Vision and Image Understanding, Volume 117, Issue 9, September 2013, Page 1004-1016 ELSERVIER
- Martins LDO, Junior GB, Silva AC, Paiva ACD, Gattass M, “Detection of Masses in Digital Mammograms using K-means and Support Vector Machine.” Electronic Letters on Computer Vision and Image Analysis 8(2):39-50.
- A. Bhardwaj and J.S. Bhatia, “An Image Segmentation Method for Early Detection and Analysis of Melanoma.” 2279-0853, p-ISSN: 2279-0861.Volume 13.IOSR 2013.
- S. Wold, K. Esbensen, P. Geladi, “Principal component analysis.” Chemometrics and intelligent laboratory systems 2(1):37-52.
- Rafael C. Gonzalez, Richard E. Woods, Steven L. Eddins, “Digital Image Processing Using MATLAB”, Third Edition Tata McGraw Hill Pvt. Ltd., 2011
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