Abstract #1064
Comparative Study of Four Widely used Classifiers for Prostate Cancer Detection with Multi-parametric MRI
Chengyan Wang 1 , Shuangjuan Cheng 2 , Juan Hu 2 , He Wang 2 , Xuedong Yang 2 , Jue Zhang 1,3 , Xiaoying Wang 1,2 , and Jing Fang 1,3
1
Academy for Advanced Interdisciplinary
Studies, Peking University, Beijing, Beijing, China,
2
Department
of Radiology, Peking University First Hospital, Beijing,
Beijing, China,
3
College of Enigneering,
Peking University, Beijing, Beijing, China
Prostate cancer (PCa) is the second most frequently
diagnosed cancer and the sixth leading cause of cancer
death among men worldwide [1]. Several studies [2-4]
have proven that the diagnostic accuracy of PCa
detection can be significantly improved by combining
different MR sequences, and several computer-aided
diagnosis (CAD) systems have been proposed to integrate
the MR information. However, no comparison has been made
to find out which system performs better. In this study,
we evaluate the performance of four widely used
classifiers using leave-one-out (LOO) method. MLP and
SVM classifiers which have been successfully applied in
many branches of medical diagnostics seem promising
here. Because of their abilities to resolve nonlinear
complex relations among input variables, without the
need for any prior assumptions about these relations,
MLP and SVM models are more advisable for PCa detection.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
