Computer-aided diagnosis in Brain Computer Tomography screening
Main Author: | |
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Publication Date: | 2009 |
Other Authors: | |
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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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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info:eu-repo/semantics/openAccess |
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openAccess |
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application/pdf |
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Springer |
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Springer |
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