DocumentCode
2940589
Title
Automatically Early Detection of Skin Cancer: Study Based on Nueral Netwok Classification
Author
Lau, Ho Tak ; Al-Jumaily, Adel
Author_Institution
Sch. of Electr., Mech. & Mechatron. Syst., Univ. of Technol., Sydney, NSW, Australia
fYear
2009
fDate
4-7 Dec. 2009
Firstpage
375
Lastpage
380
Abstract
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.
Keywords
backpropagation; cancer; image classification; medical image processing; neural nets; object detection; skin; 3 layers backpropagation neural network classifier; automatically early skin cancer detection; automatically skin cancer classification system; dermoscopy photo; image database; image processing procedure; neural network classification; Cancer detection; Skin cancer; Skin cancer; classification; computer based detection; neural network;
fLanguage
English
Publisher
ieee
Conference_Titel
Soft Computing and Pattern Recognition, 2009. SOCPAR '09. International Conference of
Conference_Location
Malacca
Print_ISBN
978-1-4244-5330-6
Electronic_ISBN
978-0-7695-3879-2
Type
conf
DOI
10.1109/SoCPaR.2009.80
Filename
5370977
Link To Document