Title :
Classification of prostate tissue using neural networks
Author :
Gnadt, W. ; Manolakis, D. ; Feleppa, E. ; Lizzi, F. ; Liu, T. ; Lee, P.
Author_Institution :
Riverside Res. Inst., Lexington, MA, USA
Abstract :
This paper presents the development of a method for distinguishing between cancerous and non-cancerous tissue of the prostate based on spectrum analysis of ultrasound radio-frequency echo-signals. The classification of prostate tissue is performed using the neural network technology. At this stage, the image segmentation and feature extraction processes are well established. Hence, we focus here on the method of selection, training and evaluation of a neural network classifier. Results from well-known statistical methods are provided for comparison. Our results demonstrate the viability both of our approach and of this technology when operating on real-world data
Keywords :
feature extraction; image segmentation; learning (artificial intelligence); medical diagnostic computing; neural nets; pattern classification; RF ultrasound echo-signals; feature extraction; image segmentation; learning; medical computing; pattern classification; prostate tissue; spectrum analysis; Biopsy; Cancer; Databases; Feature extraction; Neural networks; Radio frequency; Scattering; Statistical analysis; Testing; Ultrasonic imaging;
Conference_Titel :
Neural Networks, 1999. IJCNN '99. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5529-6
DOI :
10.1109/IJCNN.1999.836244