Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques
Autor(a) principal: | |
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Data de Publicação: | 2022 |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Brazilian Archives of Biology and Technology |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100601 |
Resumo: | Abstract Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray. However, a sufficient number of radiologists are needed to interpret the X-ray images. High rates of child deaths due to pneumonia have been encountered. Using this type of system, a diagnosis can be made quickly, and then the treatment process can be started rapidly. This study aims to diagnose pneumonia using boosting techniques by the automatic tool. With this tool, the workload of the doctors/radiologists is reduced. The boosting techniques are a family of machine learning techniques. Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are used for the study. These techniques are chosen because of their simulation duration for modeling and convenience for real-time applications. L2 normalization and feature selection are applied to the data before applying the techniques. Random Forest Classifier is used for feature selection estimator. After the modeling, Categorical Boosting algorithm is observed as faster than the other techniques. Simulation duration is obtained as 0.7 seconds. By using this automatic tool, the user can be able to upload the desired X-ray image to the system and get the result easily from the screen without any radiologist/doctor. |
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Brazilian Archives of Biology and Technology |
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Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting TechniquesCategorical boostingextreme gradient boostinggradient boostinglight gradient boostingmachine learningpneumoniauser interface toolAbstract Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray. However, a sufficient number of radiologists are needed to interpret the X-ray images. High rates of child deaths due to pneumonia have been encountered. Using this type of system, a diagnosis can be made quickly, and then the treatment process can be started rapidly. This study aims to diagnose pneumonia using boosting techniques by the automatic tool. With this tool, the workload of the doctors/radiologists is reduced. The boosting techniques are a family of machine learning techniques. Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are used for the study. These techniques are chosen because of their simulation duration for modeling and convenience for real-time applications. L2 normalization and feature selection are applied to the data before applying the techniques. Random Forest Classifier is used for feature selection estimator. After the modeling, Categorical Boosting algorithm is observed as faster than the other techniques. Simulation duration is obtained as 0.7 seconds. By using this automatic tool, the user can be able to upload the desired X-ray image to the system and get the result easily from the screen without any radiologist/doctor.Instituto de Tecnologia do Paraná - Tecpar2022-01-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100601Brazilian Archives of Biology and Technology v.65 2022reponame:Brazilian Archives of Biology and Technologyinstname:Instituto de Tecnologia do Paraná (Tecpar)instacron:TECPAR10.1590/1678-4324-2022210322info:eu-repo/semantics/openAccessPostalcioglu,Sedaeng2022-03-18T00:00:00Zoai:scielo:S1516-89132022000100601Revistahttps://www.scielo.br/j/babt/https://old.scielo.br/oai/scielo-oai.phpbabt@tecpar.br||babt@tecpar.br1678-43241516-8913opendoar:2022-03-18T00:00Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar)false |
dc.title.none.fl_str_mv |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
title |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
spellingShingle |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques Postalcioglu,Seda Categorical boosting extreme gradient boosting gradient boosting light gradient boosting machine learning pneumonia user interface tool |
title_short |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
title_full |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
title_fullStr |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
title_full_unstemmed |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
title_sort |
Design of Automatic Tool for Diagnosis of Pneumonia Using Boosting Techniques |
author |
Postalcioglu,Seda |
author_facet |
Postalcioglu,Seda |
author_role |
author |
dc.contributor.author.fl_str_mv |
Postalcioglu,Seda |
dc.subject.por.fl_str_mv |
Categorical boosting extreme gradient boosting gradient boosting light gradient boosting machine learning pneumonia user interface tool |
topic |
Categorical boosting extreme gradient boosting gradient boosting light gradient boosting machine learning pneumonia user interface tool |
description |
Abstract Covid-19 is today's pandemic disease and can cause the hospital crowded. Additionally, It affects the lungs and may cause pneumonia. The most popular technique for diagnosis of pneumonia is the evaluation of X-ray. However, a sufficient number of radiologists are needed to interpret the X-ray images. High rates of child deaths due to pneumonia have been encountered. Using this type of system, a diagnosis can be made quickly, and then the treatment process can be started rapidly. This study aims to diagnose pneumonia using boosting techniques by the automatic tool. With this tool, the workload of the doctors/radiologists is reduced. The boosting techniques are a family of machine learning techniques. Gradient Boosting Machine (GBM), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Boosting (CatBoost) are used for the study. These techniques are chosen because of their simulation duration for modeling and convenience for real-time applications. L2 normalization and feature selection are applied to the data before applying the techniques. Random Forest Classifier is used for feature selection estimator. After the modeling, Categorical Boosting algorithm is observed as faster than the other techniques. Simulation duration is obtained as 0.7 seconds. By using this automatic tool, the user can be able to upload the desired X-ray image to the system and get the result easily from the screen without any radiologist/doctor. |
publishDate |
2022 |
dc.date.none.fl_str_mv |
2022-01-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100601 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-89132022000100601 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1678-4324-2022210322 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
publisher.none.fl_str_mv |
Instituto de Tecnologia do Paraná - Tecpar |
dc.source.none.fl_str_mv |
Brazilian Archives of Biology and Technology v.65 2022 reponame:Brazilian Archives of Biology and Technology instname:Instituto de Tecnologia do Paraná (Tecpar) instacron:TECPAR |
instname_str |
Instituto de Tecnologia do Paraná (Tecpar) |
instacron_str |
TECPAR |
institution |
TECPAR |
reponame_str |
Brazilian Archives of Biology and Technology |
collection |
Brazilian Archives of Biology and Technology |
repository.name.fl_str_mv |
Brazilian Archives of Biology and Technology - Instituto de Tecnologia do Paraná (Tecpar) |
repository.mail.fl_str_mv |
babt@tecpar.br||babt@tecpar.br |
_version_ |
1750318281645883392 |