Classifier combination for in vivo magnetic resonance spectra of brain tumours
In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in...
| Autores: | , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2002 |
| País: | España |
| Institución: | Universitat Oberta de Catalunya (UOC) |
| Repositorio: | O2, repositorio institucional de la UOC |
| OAI Identifier: | oai:openaccess.uoc.edu:10609/17001 |
| Acceso en línea: | http://hdl.handle.net/10609/17001 |
| Access Level: | acceso abierto |
| Palabra clave: | ressonància magnètica tumors cerebrals radiologia magnetic resonance imaging brain tumors radiology resonancia magnética tumores cerebrales radiología Radiologia Radiología Radiology |
| Sumario: | In this paper we present a multi-stage classifier for magnetic resonance spectra of human brain tumours which is being developed as part of a decision support system for radiologists. The basic idea is to decompose a complex classification scheme into a sequence of classifiers, each specialising in different classes of tumours and trying to reproduce part of the WHO classification hierarchy. Each stage uses a particular set of classification features, which are selected using a combination of classical statistical analysis, splitting performance and previous knowledge. Classifiers with different behaviour are combined using a simple voting scheme in order to extract different error patterns: LDA, decision trees and the k-NN classifier. A special label named "unknown" is used when the outcomes of the different classifiers disagree. Cascading is also used to incorporate class distances computed using LDA into decision trees. Both cascading and voting are effective tools to improve classification accuracy. Experiments also show that it is possible to extract useful information from the classification process itself in order to help users (clinicians and radiologists) to make more accurate predictions and reduce the number of possible classification mistakes. |
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