Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit

Patients in the intensive care unit require fast and efficient handling, including in-diagnosis service. The objectives of this study are to produce a computer-aided system so that it can help radiologists to classify the types of brain tumors suffered by patients quickly and accurately; to build ap...

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Autores principales: Fahmi, Fahmi, Apriyulida, Fitri, Nasution, Irina Kemala, Sawaluddin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi 2020
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378674/
https://www.ncbi.nlm.nih.gov/pubmed/32733660
http://dx.doi.org/10.1155/2020/2483285
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author Fahmi, Fahmi
Apriyulida, Fitri
Nasution, Irina Kemala
Sawaluddin,
author_facet Fahmi, Fahmi
Apriyulida, Fitri
Nasution, Irina Kemala
Sawaluddin,
author_sort Fahmi, Fahmi
collection PubMed
description Patients in the intensive care unit require fast and efficient handling, including in-diagnosis service. The objectives of this study are to produce a computer-aided system so that it can help radiologists to classify the types of brain tumors suffered by patients quickly and accurately; to build applications that can determine the location of brain tumors from CT scan images; and to get the results of the analysis of the system design. The combination of the zoning algorithm with Learning Vector Quantization can increase the speed of computing and can classify normal and abnormal brains with an average accuracy of 85%.
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spelling pubmed-73786742020-07-29 Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit Fahmi, Fahmi Apriyulida, Fitri Nasution, Irina Kemala Sawaluddin, J Healthc Eng Research Article Patients in the intensive care unit require fast and efficient handling, including in-diagnosis service. The objectives of this study are to produce a computer-aided system so that it can help radiologists to classify the types of brain tumors suffered by patients quickly and accurately; to build applications that can determine the location of brain tumors from CT scan images; and to get the results of the analysis of the system design. The combination of the zoning algorithm with Learning Vector Quantization can increase the speed of computing and can classify normal and abnormal brains with an average accuracy of 85%. Hindawi 2020-07-14 /pmc/articles/PMC7378674/ /pubmed/32733660 http://dx.doi.org/10.1155/2020/2483285 Text en Copyright © 2020 Fahmi Fahmi et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Fahmi, Fahmi
Apriyulida, Fitri
Nasution, Irina Kemala
Sawaluddin,
Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
title Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
title_full Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
title_fullStr Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
title_full_unstemmed Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
title_short Automatic Detection of Brain Tumor on Computed Tomography Images for Patients in the Intensive Care Unit
title_sort automatic detection of brain tumor on computed tomography images for patients in the intensive care unit
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7378674/
https://www.ncbi.nlm.nih.gov/pubmed/32733660
http://dx.doi.org/10.1155/2020/2483285
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