Paper Info
Title
Quality Assurance and Increased Efficiency in Medical Projects with Neural Networks by Using a Structured Development Method for Feedforward Neural Networks (SENN)
Abstract
The growing number of projects using neural networks in medical care makes it necessary to examine how productivity can be increased and how quality can be assured. This examination addresses the problem specification, data preparation as well as the development of appropriate representations, the selection of suitable encodings and the combination of encodings. Network paradigms with fast learning properties and network structures that can be analysed and interpreted after the training process have been successfully applied to medical tasks. The associative recall of examples (ARE) can be used to verify the quality of representations and encodings. Furthermore it is possible to evaluate the competence of a neural network for a specific task by an ARE. Finally, a standard approach and the hereafter presented method applied for a medical project are compared. The comparison of these two approaches and the collection of the medical data is part of the DIADOQ-project.
Year
DOI
Venue
1995
10.1007/3-540-60025-6_150
AIME '87
Keywords
Field
DocType
medical projects,structured development method,quality assurance,feedforward neural networks,feedforward neural network,neural network
Data mining,Feedforward neural network,Computer science,Artificial intelligence,System lifecycle,Artificial neural network,Data preparation,Associative recall,Machine learning,Quality assurance,Network structure
Conference
Volume
ISBN
Citations 
934
3-540-60025-6
0
PageRank 
References 
Authors
0.34
7
8
Name
Order
Citations
PageRank
T. Walschulzik100.34
Wilfried Brauer2969299.36
M. Förster321.20
K. Kirchner420.86
Rolf Engelbrecht502.70
T. Schütz600.68
T. Koschinsky720.86
G. Entenmann820.86