DocumentCode
1857423
Title
Notice of Retraction
Based on Support Vector Machine´s Tumor Image Classifier Design
Author
Lan Gan ; Zhongping Yu
Author_Institution
Sch. of Inf. Eng., East China Jiaotong Univ., Nanchang, China
fYear
2010
fDate
22-24 Jan. 2010
Firstpage
137
Lastpage
140
Abstract
Notice of Retraction
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we designed a tumor image classification and recognition system. With a series approaches of image pre-processing, segmentation and tracking, extract the global image characteristic parameters ( the characteristics of the whole picture). According to obtain the characteristic parameters to realize the image classification and identification. Since this article mainly related to small sample data, so the use of Fisher method, KNN method and support vector machine (SVM) Comparison of three methods of classification and recognition rate, experimental results show that the Support Vector Machine (SVM) classification recognition rate higher, more reliable.
After careful and considered review of the content of this paper by a duly constituted expert committee, this paper has been found to be in violation of IEEE´s Publication Principles.
We hereby retract the content of this paper. Reasonable effort should be made to remove all past references to this paper.
The presenting author of this paper has the option to appeal this decision by contacting TPII@ieee.org.
In this paper, we designed a tumor image classification and recognition system. With a series approaches of image pre-processing, segmentation and tracking, extract the global image characteristic parameters ( the characteristics of the whole picture). According to obtain the characteristic parameters to realize the image classification and identification. Since this article mainly related to small sample data, so the use of Fisher method, KNN method and support vector machine (SVM) Comparison of three methods of classification and recognition rate, experimental results show that the Support Vector Machine (SVM) classification recognition rate higher, more reliable.
Keywords
image classification; image segmentation; medical image processing; support vector machines; tumours; Fisher method; KNN method; global image characteristic parameters extraction; image preprocessing; image segmentation; image tracking; support vector machine; tumor image classification system design; tumor image recognition system design; Biomedical imaging; Cancer; Classification tree analysis; Design engineering; Feature extraction; Image classification; Image recognition; Neoplasms; Support vector machine classification; Support vector machines; Fisher method; Global characteristics; KNN method; support vector machine;
fLanguage
English
Publisher
ieee
Conference_Titel
e-Education, e-Business, e-Management, and e-Learning, 2010. IC4E '10. International Conference on
Conference_Location
Sanya
Print_ISBN
978-1-4244-5680-2
Type
conf
DOI
10.1109/IC4E.2010.48
Filename
5432368
Link To Document