Quality assurance and increased efficiency in medical projects with neural networks by using a structured development method for feedforward neural networks (SENN)

Autor:	T. Koschinsky, M. Förster, Wilfried Brauer, Rolf Engelbrecht, G. Entenmann, T. Schütz, T. Walschulzik, K. Kirchner
Rok vydání:	1995
Předmět:	Artificial neural network Computer science business.industry System lifecycle Machine learning computer.software_genre Associative recall Medical care Data preparation Feedforward neural network Artificial intelligence business Competence (human resources) computer Quality assurance
Zdroj:	Artificial Intelligence in Medicine ISBN: 9783540600251 AIME
DOI:	10.1007/3-540-60025-6_150
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::75a8e836831e3fd5bdc79ca07b11b576 https://doi.org/10.1007/3-540-60025-6_150 Zobrazit plný text záznamu