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Artificial neural networks in medical diagnosis
Filippo Amato, Alberto López, Eladia María Peña-Méndez, Petr Vaňhara, Aleš Hampl, Josef Havel
Language English Country Czech Republic
Document type Introductory Journal Article, Research Support, Non-U.S. Gov't
NLK
Free Medical Journals
from 2003 to 2013
Freely Accessible Science Journals
from 2003 to 2013
ROAD: Directory of Open Access Scholarly Resources
from 2002
- MeSH
- Databases as Topic MeSH
- Diabetes Mellitus diagnosis MeSH
- Diagnostic Techniques and Procedures * trends MeSH
- Cardiovascular Diseases diagnosis MeSH
- Neoplasms diagnosis MeSH
- Neural Networks, Computer * MeSH
- Decision Theory MeSH
- Artificial Intelligence MeSH
- Publication type
- Research Support, Non-U.S. Gov't MeSH
- Introductory Journal Article MeSH
An extensive amount of information is currently available to clinical specialists, ranging from details of clinical symptoms to various types of biochemical data and outputs of imaging devices. Each type of data provides information that must be evaluated and assigned to a particular pathology during the diagnostic process. To streamline the diagnostic process in daily routine and avoid misdiagnosis, artificial intelligence methods (especially computer aided diagnosis and artificial neural networks) can be employed. These adaptive learning algorithms can handle diverse types of medical data and integrate them into categorized outputs. In this paper, we briefly review and discuss the philosophy, capabilities, and limitations of artificial neural networks in medical diagnosis through selected exampl
Department of Chemistry Faculty of Science Masaryk University Brno Czech Republic
Department of Histology and Embryology Faculty of Medicine Masaryk University Brno Czech Republic
Department of Physical Electronics Faculty of Science Masaryk University Brno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Czech Republic
References provided by Crossref.org
Literatura
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