Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks
Descripción del Articulo
We want to thank the Image Processing Research Laboratory. (INTI-Lab) and the Universidad de Ciencias y Humanidades. (UCH) for their support in this research, the National Fund for. Scientific, Technological and Technological Innovation (FONDECYT), according to the research: ?SAMAYCOV: ?Desarrollo d...
| Autores: | , , , , , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2021 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/2963 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/2963 https://doi.org/10.14569/IJACSA.2021.0120645 |
| Nivel de acceso: | acceso abierto |
| Materia: | pneumonia Algorithm classification computational neural networks lung sounds mortality https://purl.org/pe-repo/ocde/ford#3.02.28 |
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| dc.title.none.fl_str_mv |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| title |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| spellingShingle |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks Alicia A.M. pneumonia Algorithm Algorithm classification classification computational neural networks computational neural networks lung sounds mortality mortality https://purl.org/pe-repo/ocde/ford#3.02.28 |
| title_short |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| title_full |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| title_fullStr |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| title_full_unstemmed |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| title_sort |
Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks |
| author |
Alicia A.M. |
| author_facet |
Alicia A.M. Alexander A.-G. Sebastian R.-C. William C.F. Michael C.-T. Víctor H.-A. |
| author_role |
author |
| author2 |
Alexander A.-G. Sebastian R.-C. William C.F. Michael C.-T. Víctor H.-A. |
| author2_role |
author author author author author |
| dc.contributor.author.fl_str_mv |
Alicia A.M. Alexander A.-G. Sebastian R.-C. William C.F. Michael C.-T. Víctor H.-A. |
| dc.subject.none.fl_str_mv |
pneumonia |
| topic |
pneumonia Algorithm Algorithm classification classification computational neural networks computational neural networks lung sounds mortality mortality https://purl.org/pe-repo/ocde/ford#3.02.28 |
| dc.subject.es_PE.fl_str_mv |
Algorithm Algorithm classification classification computational neural networks computational neural networks lung sounds mortality mortality |
| dc.subject.ocde.none.fl_str_mv |
https://purl.org/pe-repo/ocde/ford#3.02.28 |
| description |
We want to thank the Image Processing Research Laboratory. (INTI-Lab) and the Universidad de Ciencias y Humanidades. (UCH) for their support in this research, the National Fund for. Scientific, Technological and Technological Innovation (FONDECYT), according to the research: ?SAMAYCOV: ?Desarrollo de un dispositivo electr?nico port?til a bajo costo para evaluar riesgo de neumon?a basado en sonido pulmonar anormal en pacientes con sospecha de COVID-19 en zonas vulnerables?. CONVENIO 054-2020-FONDECYT?; for the financing of this research and the Electronics Laboratory of the UCH for assigning us their facilities and being able to carry out the respective tests. |
| publishDate |
2021 |
| dc.date.accessioned.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.available.none.fl_str_mv |
2024-05-30T23:13:38Z |
| dc.date.issued.fl_str_mv |
2021 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/2963 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.14569/IJACSA.2021.0120645 |
| dc.identifier.scopus.none.fl_str_mv |
2-s2.0-85109194641 |
| url |
https://hdl.handle.net/20.500.12390/2963 https://doi.org/10.14569/IJACSA.2021.0120645 |
| identifier_str_mv |
2-s2.0-85109194641 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
International Journal of Advanced Computer Science and Applications |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| dc.rights.uri.none.fl_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| eu_rights_str_mv |
openAccess |
| rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| dc.publisher.none.fl_str_mv |
Science and Information Organization |
| publisher.none.fl_str_mv |
Science and Information Organization |
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reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
| instacron_str |
CONCYTEC |
| institution |
CONCYTEC |
| reponame_str |
CONCYTEC-Institucional |
| collection |
CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
| _version_ |
1839175713612103680 |
| spelling |
Publicationrp08389600rp08392600rp08391600rp08393600rp08390600rp08394600Alicia A.M.Alexander A.-G.Sebastian R.-C.William C.F.Michael C.-T.Víctor H.-A.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2021https://hdl.handle.net/20.500.12390/2963https://doi.org/10.14569/IJACSA.2021.01206452-s2.0-85109194641We want to thank the Image Processing Research Laboratory. (INTI-Lab) and the Universidad de Ciencias y Humanidades. (UCH) for their support in this research, the National Fund for. Scientific, Technological and Technological Innovation (FONDECYT), according to the research: ?SAMAYCOV: ?Desarrollo de un dispositivo electr?nico port?til a bajo costo para evaluar riesgo de neumon?a basado en sonido pulmonar anormal en pacientes con sospecha de COVID-19 en zonas vulnerables?. CONVENIO 054-2020-FONDECYT?; for the financing of this research and the Electronics Laboratory of the UCH for assigning us their facilities and being able to carry out the respective tests.In the world and in Peru, Acute Respiratory Infections are the main cause of death, especially in the most vulnerable population, children under 5 years of age and older adults. Pneumonia is the leading cause of death of children in the world. 60.2% of pneumonia cases affect children under 5 years of age. Thus, prevention and timely treatment of lung diseases are crucial to reduce infant mortality in Peru. Among the main problems associated with this high is percentage the lack of medical professionals and resources, especially in remote areas, such as Puno, Huancavelica and Arequipa, which experience temperatures as low as -20°C during the cold season. This study develops an algorithm based on computational neural networks to differentiate between normal and abnormal lung sounds. The initial base of 917 sounds was used, through a process of data augmentation, this base was increased to 8253 sounds in total, and this process was carried out due to the need of a large number of data for the use of computational neural networks. From each signal, features were extracted using three methods: MFCC, Melspectogram and STFT. Three models were generated, the first one to classify normal and abnormal, which obtained a training Accuracy of 1 and a testing accuracy of 0.998. The second one classifies normal sound, pneumonia and other abnormalities and obtained training Accuracy values of 0.9959 and a testing accuracy of 0.9885. Finally, we classified by specific ailment where we obtained a training Accuracy of 0.9967 and a testing accuracy of 0.9909. This research provides interesting findings about the diagnosis and classification of lung sounds automatically using convolutional neural networks, which is the beginning for the development of a platform to assess the risk of pneumonia in the first moment, thus allowing rapid care and referral that seeks to reduce mortality associated mainly with pneumonia. © 2021Consejo Nacional de Ciencia, Tecnología e Innovación Tecnológica - ConcytecengScience and Information OrganizationInternational Journal of Advanced Computer Science and Applicationsinfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/4.0/pneumoniaAlgorithm-1Algorithm-1classification-1classification-1computational neural networks-1computational neural networks-1lung sounds-1mortality-1mortality-1https://purl.org/pe-repo/ocde/ford#3.02.28-1Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networksinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC20.500.12390/2963oai:repositorio.concytec.gob.pe:20.500.12390/29632024-05-30 16:12:33.582https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_14cbinfo:eu-repo/semantics/closedAccessmetadata only accesshttps://repositorio.concytec.gob.peRepositorio Institucional CONCYTECrepositorio@concytec.gob.pe#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="b7811d41-f334-4f3b-9c8a-786b57acc089"> <Type xmlns="https://www.openaire.eu/cerif-profile/vocab/COAR_Publication_Types">http://purl.org/coar/resource_type/c_1843</Type> <Language>eng</Language> <Title>Abnormal Pulmonary Sounds Classification Algorithm using Convolutional Networks</Title> <PublishedIn> <Publication> <Title>International Journal of Advanced Computer Science and Applications</Title> </Publication> </PublishedIn> <PublicationDate>2021</PublicationDate> <DOI>https://doi.org/10.14569/IJACSA.2021.0120645</DOI> <SCP-Number>2-s2.0-85109194641</SCP-Number> <Authors> <Author> <DisplayName>Alicia A.M.</DisplayName> <Person id="rp08389" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Alexander A.-G.</DisplayName> <Person id="rp08392" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Sebastian R.-C.</DisplayName> <Person id="rp08391" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>William C.F.</DisplayName> <Person id="rp08393" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Michael C.-T.</DisplayName> <Person id="rp08390" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Víctor H.-A.</DisplayName> <Person id="rp08394" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Science and Information Organization</DisplayName> <OrgUnit /> </Publisher> </Publishers> <License>https://creativecommons.org/licenses/by-nc-nd/4.0/</License> <Keyword>pneumonia</Keyword> <Keyword>Algorithm</Keyword> <Keyword>Algorithm</Keyword> <Keyword>classification</Keyword> <Keyword>classification</Keyword> <Keyword>computational neural networks</Keyword> <Keyword>computational neural networks</Keyword> <Keyword>lung sounds</Keyword> <Keyword>mortality</Keyword> <Keyword>mortality</Keyword> <Abstract>In the world and in Peru, Acute Respiratory Infections are the main cause of death, especially in the most vulnerable population, children under 5 years of age and older adults. Pneumonia is the leading cause of death of children in the world. 60.2% of pneumonia cases affect children under 5 years of age. Thus, prevention and timely treatment of lung diseases are crucial to reduce infant mortality in Peru. Among the main problems associated with this high is percentage the lack of medical professionals and resources, especially in remote areas, such as Puno, Huancavelica and Arequipa, which experience temperatures as low as -20°C during the cold season. This study develops an algorithm based on computational neural networks to differentiate between normal and abnormal lung sounds. The initial base of 917 sounds was used, through a process of data augmentation, this base was increased to 8253 sounds in total, and this process was carried out due to the need of a large number of data for the use of computational neural networks. From each signal, features were extracted using three methods: MFCC, Melspectogram and STFT. Three models were generated, the first one to classify normal and abnormal, which obtained a training Accuracy of 1 and a testing accuracy of 0.998. The second one classifies normal sound, pneumonia and other abnormalities and obtained training Accuracy values of 0.9959 and a testing accuracy of 0.9885. Finally, we classified by specific ailment where we obtained a training Accuracy of 0.9967 and a testing accuracy of 0.9909. This research provides interesting findings about the diagnosis and classification of lung sounds automatically using convolutional neural networks, which is the beginning for the development of a platform to assess the risk of pneumonia in the first moment, thus allowing rapid care and referral that seeks to reduce mortality associated mainly with pneumonia. © 2021</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
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13.243791 |
Nota importante:
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
La información contenida en este registro es de entera responsabilidad de la institución que gestiona el repositorio institucional donde esta contenido este documento o set de datos. El CONCYTEC no se hace responsable por los contenidos (publicaciones y/o datos) accesibles a través del Repositorio Nacional Digital de Ciencia, Tecnología e Innovación de Acceso Abierto (ALICIA).
