Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders
Descripción del Articulo
This work has been partially funded by the Master Scholarship at the Universidad Nacional de San Agustín, which is an initiative of CITEC through a fund FONDECYT (Perú). We would like to thank research department of Instituto Nacional de Enfermedades Neoplásicas from Peru, for gently providing us hi...
| Autores: | , , |
|---|---|
| Formato: | artículo |
| Fecha de Publicación: | 2018 |
| Institución: | Consejo Nacional de Ciencia Tecnología e Innovación |
| Repositorio: | CONCYTEC-Institucional |
| Lenguaje: | inglés |
| OAI Identifier: | oai:repositorio.concytec.gob.pe:20.500.12390/2309 |
| Enlace del recurso: | https://hdl.handle.net/20.500.12390/2309 https://doi.org/10.1007/978-3-319-75193-1_20 |
| Nivel de acceso: | acceso abierto |
| Materia: | Skin cancer Autoencoders Convolutional neural networks Image classification http://purl.org/pe-repo/ocde/ford#3.03.08 |
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| dc.title.none.fl_str_mv |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| title |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| spellingShingle |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders Coronado R. Skin cancer Autoencoders Convolutional neural networks Image classification http://purl.org/pe-repo/ocde/ford#3.03.08 |
| title_short |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| title_full |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| title_fullStr |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| title_full_unstemmed |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| title_sort |
Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders |
| author |
Coronado R. |
| author_facet |
Coronado R. Ocsa A. Quispe O. |
| author_role |
author |
| author2 |
Ocsa A. Quispe O. |
| author2_role |
author author |
| dc.contributor.author.fl_str_mv |
Coronado R. Ocsa A. Quispe O. |
| dc.subject.none.fl_str_mv |
Skin cancer |
| topic |
Skin cancer Autoencoders Convolutional neural networks Image classification http://purl.org/pe-repo/ocde/ford#3.03.08 |
| dc.subject.es_PE.fl_str_mv |
Autoencoders Convolutional neural networks Image classification |
| dc.subject.ocde.none.fl_str_mv |
http://purl.org/pe-repo/ocde/ford#3.03.08 |
| description |
This work has been partially funded by the Master Scholarship at the Universidad Nacional de San Agustín, which is an initiative of CITEC through a fund FONDECYT (Perú). We would like to thank research department of Instituto Nacional de Enfermedades Neoplásicas from Peru, for gently providing us his advice on the direction of this article. |
| publishDate |
2018 |
| 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 |
2018 |
| dc.type.none.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12390/2309 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/978-3-319-75193-1_20 |
| dc.identifier.scopus.none.fl_str_mv |
2-s2.0-85042215814 |
| url |
https://hdl.handle.net/20.500.12390/2309 https://doi.org/10.1007/978-3-319-75193-1_20 |
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2-s2.0-85042215814 |
| dc.language.iso.none.fl_str_mv |
eng |
| language |
eng |
| dc.relation.ispartof.none.fl_str_mv |
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
| dc.rights.none.fl_str_mv |
info:eu-repo/semantics/openAccess |
| eu_rights_str_mv |
openAccess |
| dc.publisher.none.fl_str_mv |
Springer Verlag |
| publisher.none.fl_str_mv |
Springer Verlag |
| dc.source.none.fl_str_mv |
reponame:CONCYTEC-Institucional instname:Consejo Nacional de Ciencia Tecnología e Innovación instacron:CONCYTEC |
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Consejo Nacional de Ciencia Tecnología e Innovación |
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CONCYTEC |
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CONCYTEC |
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CONCYTEC-Institucional |
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CONCYTEC-Institucional |
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Repositorio Institucional CONCYTEC |
| repository.mail.fl_str_mv |
repositorio@concytec.gob.pe |
| _version_ |
1839175490695331840 |
| spelling |
Publicationrp01522600rp01524600rp01523600Coronado R.Ocsa A.Quispe O.2024-05-30T23:13:38Z2024-05-30T23:13:38Z2018https://hdl.handle.net/20.500.12390/2309https://doi.org/10.1007/978-3-319-75193-1_202-s2.0-85042215814This work has been partially funded by the Master Scholarship at the Universidad Nacional de San Agustín, which is an initiative of CITEC through a fund FONDECYT (Perú). We would like to thank research department of Instituto Nacional de Enfermedades Neoplásicas from Peru, for gently providing us his advice on the direction of this article.Every year, people around the world are affected by different skin diseases or cancer. Nowadays, these can only be detected accurately by clinical analysis and skin biopsy. However, the diagnosis of this malignant disease does not ensure the survival of the patient, since many clinical cases are detected in the terminal phases. Only early diagnosis would increase the life expectancy of patients. In this paper, we propose a method to recognition malignant skin diseases to identify malignant lesions in non-dermatoscopic images. For the method, we use Convolutional Neural Network and propose the use of autoencoders as another classification model that provides more information on the diagnosis. Experiments show that our proposal reaches up to 84.4% of accuracy in the well-known dataset of the ISIC-2016. In addition, we collect non-dermatoscopic images of skin lesions and developed a new dataset to demonstrate the advantage of our method. © Springer International Publishing AG, part of Springer Nature 2018.Fondo Nacional de Desarrollo Científico y Tecnológico - FondecytengSpringer VerlagLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)info:eu-repo/semantics/openAccessSkin cancerAutoencoders-1Convolutional neural networks-1Image classification-1http://purl.org/pe-repo/ocde/ford#3.03.08-1Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencodersinfo:eu-repo/semantics/articlereponame:CONCYTEC-Institucionalinstname:Consejo Nacional de Ciencia Tecnología e Innovacióninstacron:CONCYTEC#PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE##PLACEHOLDER_PARENT_METADATA_VALUE#20.500.12390/2309oai:repositorio.concytec.gob.pe:20.500.12390/23092024-05-30 15:42:09.146http://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#<Publication xmlns="https://www.openaire.eu/cerif-profile/1.1/" id="3376daff-6e21-4a7c-8785-c675d8b208e0"> <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>Non-dermatoscopic image analysis for the recognition of malignant skin diseases with convolutional neural network and autoencoders</Title> <PublishedIn> <Publication> <Title>Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)</Title> </Publication> </PublishedIn> <PublicationDate>2018</PublicationDate> <DOI>https://doi.org/10.1007/978-3-319-75193-1_20</DOI> <SCP-Number>2-s2.0-85042215814</SCP-Number> <Authors> <Author> <DisplayName>Coronado R.</DisplayName> <Person id="rp01522" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Ocsa A.</DisplayName> <Person id="rp01524" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> <Author> <DisplayName>Quispe O.</DisplayName> <Person id="rp01523" /> <Affiliation> <OrgUnit> </OrgUnit> </Affiliation> </Author> </Authors> <Editors> </Editors> <Publishers> <Publisher> <DisplayName>Springer Verlag</DisplayName> <OrgUnit /> </Publisher> </Publishers> <Keyword>Skin cancer</Keyword> <Keyword>Autoencoders</Keyword> <Keyword>Convolutional neural networks</Keyword> <Keyword>Image classification</Keyword> <Abstract>Every year, people around the world are affected by different skin diseases or cancer. Nowadays, these can only be detected accurately by clinical analysis and skin biopsy. However, the diagnosis of this malignant disease does not ensure the survival of the patient, since many clinical cases are detected in the terminal phases. Only early diagnosis would increase the life expectancy of patients. In this paper, we propose a method to recognition malignant skin diseases to identify malignant lesions in non-dermatoscopic images. For the method, we use Convolutional Neural Network and propose the use of autoencoders as another classification model that provides more information on the diagnosis. Experiments show that our proposal reaches up to 84.4% of accuracy in the well-known dataset of the ISIC-2016. In addition, we collect non-dermatoscopic images of skin lesions and developed a new dataset to demonstrate the advantage of our method. © Springer International Publishing AG, part of Springer Nature 2018.</Abstract> <Access xmlns="http://purl.org/coar/access_right" > </Access> </Publication> -1 |
| score |
13.439043 |
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).
