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2009 International Conference of Soft Computing and Pattern Recognition
Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification
Malacca, Malaysia
December 04-December 07
ISBN: 978-0-7695-3879-2
| ASCII Text | x | ||
| Ho Tak Lau, Adel Al-Jumaily, "Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification," Soft Computing and Pattern Recognition, International Conference of, pp. 375-380, 2009 International Conference of Soft Computing and Pattern Recognition, 2009. | |||
| BibTex | x | ||
| @article{ 10.1109/SoCPaR.2009.80, author = {Ho Tak Lau and Adel Al-Jumaily}, title = {Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification}, journal ={Soft Computing and Pattern Recognition, International Conference of}, volume = {0}, year = {2009}, isbn = {978-0-7695-3879-2}, pages = {375-380}, doi = {http://doi.ieeecomputersociety.org/10.1109/SoCPaR.2009.80}, publisher = {IEEE Computer Society}, address = {Los Alamitos, CA, USA}, } | |||
| RefWorks Procite/RefMan/Endnote | x | ||
| TY - CONF JO - Soft Computing and Pattern Recognition, International Conference of TI - Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification SN - 978-0-7695-3879-2 SP375 EP380 A1 - Ho Tak Lau, A1 - Adel Al-Jumaily, PY - 2009 KW - Skin cancer KW - classification KW - neural network KW - computer based detection VL - 0 JA - Soft Computing and Pattern Recognition, International Conference of ER - | |||
In this paper, an automatically skin cancer classification system is developed and the relationship of skin cancer image across different type of neural network are studied with different types of preprocessing.. The collected images are feed into the system, and across different image processing procedure to enhance the image properties. Then the normal skin is removed from the skin affected area and the cancer cell is left in the image. Useful information can be extracted from these images and pass to the classification system for training and testing. Recognition accuracy of the 3-layers back-propagation neural network classifier is 89.9% and auto-associative neural network is 80.8% in the image database that include dermoscopy photo and digital photo
Index Terms:
Skin cancer, classification, neural network, computer based detection
Citation:
Ho Tak Lau, Adel Al-Jumaily, "Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification," socpar, pp.375-380, 2009 International Conference of Soft Computing and Pattern Recognition, 2009
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