Computer-aided diagnosis in Brain Computer Tomography screening

Bibliographic Details
Main Author: Peixoto, Hugo
Publication Date: 2009
Other Authors: Alves, Victor
Language: eng
Source: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
Download full: http://hdl.handle.net/1822/9734
Summary: Currently, interpretation of medical images is almost exclusively made by specialized physicians. Although, the next decades will most certainly be of change and computer-aided diagnosis systems will play an important role in the reading process. Assisted interpretation of medical images has become one of the major research subjects in medical imaging and diagnostic radiology. From a methodological point of view, the main attraction for the resolution of this kind of problem arises from the combination of the image reading made by the radiologists, with the results obtained from using Artificial Intelligence (AI) based applications that will contribute to the reduction and eventually the elimination of perception errors. This article describes how machine learning algorithms can help distinguish normal readings in Brain Computer Tomography (CT) from all its variations. The goal is to have a system that is able to identify abnormal appearing structures making the reading by the radiologist unnecessary for a large proportion of the brain CT scans.
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spelling Computer-aided diagnosis in Brain Computer Tomography screeningMedical imagingComputer aided detectionBrain Computer TomographyArtificial intelligenceMachine learningBrain Computed TomographyCurrently, interpretation of medical images is almost exclusively made by specialized physicians. Although, the next decades will most certainly be of change and computer-aided diagnosis systems will play an important role in the reading process. Assisted interpretation of medical images has become one of the major research subjects in medical imaging and diagnostic radiology. From a methodological point of view, the main attraction for the resolution of this kind of problem arises from the combination of the image reading made by the radiologists, with the results obtained from using Artificial Intelligence (AI) based applications that will contribute to the reduction and eventually the elimination of perception errors. This article describes how machine learning algorithms can help distinguish normal readings in Brain Computer Tomography (CT) from all its variations. The goal is to have a system that is able to identify abnormal appearing structures making the reading by the radiologist unnecessary for a large proportion of the brain CT scans.(undefined)SpringerUniversidade do MinhoPeixoto, HugoAlves, Victor2009-07-092009-07-09T00:00:00Zconference paperinfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/1822/9734engPERNER, Petra, ed. lit. – “Advances in Data Mining : applications and theoretical aspects : proceedings of the Industrial Conference on Data Mining, 9, Leipzig, Germany, 2009.” Heidelberg : Springer Berlin, 2009. ISBN 978-3-642-03066-6. p. 62-72.978-3-642-03066-60302-974310.1007/978-3-642-03067-3_7info:eu-repo/semantics/openAccessreponame:Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiainstacron:RCAAP2024-05-11T04:13:52Zoai:repositorium.sdum.uminho.pt:1822/9734Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireinfo@rcaap.ptopendoar:https://opendoar.ac.uk/repository/71602025-05-28T14:43:07.265619Repositórios Científicos de Acesso Aberto de Portugal (RCAAP) - FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologiafalse
dc.title.none.fl_str_mv Computer-aided diagnosis in Brain Computer Tomography screening
title Computer-aided diagnosis in Brain Computer Tomography screening
spellingShingle Computer-aided diagnosis in Brain Computer Tomography screening
Peixoto, Hugo
Medical imaging
Computer aided detection
Brain Computer Tomography
Artificial intelligence
Machine learning
Brain Computed Tomography
title_short Computer-aided diagnosis in Brain Computer Tomography screening
title_full Computer-aided diagnosis in Brain Computer Tomography screening
title_fullStr Computer-aided diagnosis in Brain Computer Tomography screening
title_full_unstemmed Computer-aided diagnosis in Brain Computer Tomography screening
title_sort Computer-aided diagnosis in Brain Computer Tomography screening
author Peixoto, Hugo
author_facet Peixoto, Hugo
Alves, Victor
author_role author
author2 Alves, Victor
author2_role author
dc.contributor.none.fl_str_mv Universidade do Minho
dc.contributor.author.fl_str_mv Peixoto, Hugo
Alves, Victor
dc.subject.por.fl_str_mv Medical imaging
Computer aided detection
Brain Computer Tomography
Artificial intelligence
Machine learning
Brain Computed Tomography
topic Medical imaging
Computer aided detection
Brain Computer Tomography
Artificial intelligence
Machine learning
Brain Computed Tomography
description Currently, interpretation of medical images is almost exclusively made by specialized physicians. Although, the next decades will most certainly be of change and computer-aided diagnosis systems will play an important role in the reading process. Assisted interpretation of medical images has become one of the major research subjects in medical imaging and diagnostic radiology. From a methodological point of view, the main attraction for the resolution of this kind of problem arises from the combination of the image reading made by the radiologists, with the results obtained from using Artificial Intelligence (AI) based applications that will contribute to the reduction and eventually the elimination of perception errors. This article describes how machine learning algorithms can help distinguish normal readings in Brain Computer Tomography (CT) from all its variations. The goal is to have a system that is able to identify abnormal appearing structures making the reading by the radiologist unnecessary for a large proportion of the brain CT scans.
publishDate 2009
dc.date.none.fl_str_mv 2009-07-09
2009-07-09T00:00:00Z
dc.type.driver.fl_str_mv conference paper
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/1822/9734
url http://hdl.handle.net/1822/9734
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv PERNER, Petra, ed. lit. – “Advances in Data Mining : applications and theoretical aspects : proceedings of the Industrial Conference on Data Mining, 9, Leipzig, Germany, 2009.” Heidelberg : Springer Berlin, 2009. ISBN 978-3-642-03066-6. p. 62-72.
978-3-642-03066-6
0302-9743
10.1007/978-3-642-03067-3_7
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eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer
publisher.none.fl_str_mv Springer
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instname:FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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instname_str FCCN, serviços digitais da FCT – Fundação para a Ciência e a Tecnologia
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