Classification of skin blemishes with cell phone images using deep learning techniques

Skin blemishes can be caused by multiple events or diseases and, in some cases, it is difficult to distinguish where they come from. Therefore, there may be cases with a dangerous origin that go unnoticed or the opposite case (which can lead to overcrowding of health services). To avoid this, the us...

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Detalles Bibliográficos
Autores: Rangel-Ramos, José Antonio, Luna Perejón, Francisco, Civit Balcells, Antón, Domínguez Morales, Manuel Jesús
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2024
País:España
Institución:Universidad de Sevilla (US)
Repositorio:idUS. Depósito de Investigación de la Universidad de Sevilla
OAI Identifier:oai:idus.us.es:11441/160923
Acceso en línea:https://hdl.handle.net/11441/160923
https://doi.org/10.1016/j.heliyon.2024.e28058
Access Level:acceso abierto
Palabra clave:Skin
Deep learning
Convolutional neural network
Artificial intelligence
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spelling Classification of skin blemishes with cell phone images using deep learning techniquesRangel-Ramos, José AntonioLuna Perejón, FranciscoCivit Balcells, AntónDomínguez Morales, Manuel JesúsSkinDeep learningConvolutional neural networkArtificial intelligenceSkin blemishes can be caused by multiple events or diseases and, in some cases, it is difficult to distinguish where they come from. Therefore, there may be cases with a dangerous origin that go unnoticed or the opposite case (which can lead to overcrowding of health services). To avoid this, the use of artificial intelligence-based classifiers using images taken with mobile devices is proposed; this would help in the initial screening process and provide some information to the patient prior to their final diagnosis. To this end, this work proposes an optimization mechanism based on two phases in which a global search for the best classifiers (from among more than 150 combinations) is carried out, and, in the second phase, the best candidates are subjected to a phase of evaluation of the robustness of the system by applying the cross-validation technique. The results obtained reach 99.95% accuracy for the best case and 99.75% AUC. Comparing the developed classifier with previous works, an improvement in terms of classification rate is appreciated, as well as in the reduction of the classifier complexity, which allows our classifier to be integrated in a specific purpose system with few computational resources.ElsevierArquitectura y Tecnología de ComputadoresTEP108: Robótica y Tecnología de ComputadoresMinisterio de Ciencia e Innovación (MICIN). España2024info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfapplication/pdfhttps://hdl.handle.net/11441/160923https://doi.org/10.1016/j.heliyon.2024.e28058reponame:idUS. Depósito de Investigación de la Universidad de Sevillainstname:Universidad de Sevilla (US)InglésPID2019-105556GB-C33https://www.sciencedirect.com/science/article/pii/S2405844024040891?via%3Dihubinfo:eu-repo/semantics/openAccessoai:idus.us.es:11441/1609232026-06-17T12:51:07Z
dc.title.none.fl_str_mv Classification of skin blemishes with cell phone images using deep learning techniques
title Classification of skin blemishes with cell phone images using deep learning techniques
spellingShingle Classification of skin blemishes with cell phone images using deep learning techniques
Rangel-Ramos, José Antonio
Skin
Deep learning
Convolutional neural network
Artificial intelligence
title_short Classification of skin blemishes with cell phone images using deep learning techniques
title_full Classification of skin blemishes with cell phone images using deep learning techniques
title_fullStr Classification of skin blemishes with cell phone images using deep learning techniques
title_full_unstemmed Classification of skin blemishes with cell phone images using deep learning techniques
title_sort Classification of skin blemishes with cell phone images using deep learning techniques
dc.creator.none.fl_str_mv Rangel-Ramos, José Antonio
Luna Perejón, Francisco
Civit Balcells, Antón
Domínguez Morales, Manuel Jesús
author Rangel-Ramos, José Antonio
author_facet Rangel-Ramos, José Antonio
Luna Perejón, Francisco
Civit Balcells, Antón
Domínguez Morales, Manuel Jesús
author_role author
author2 Luna Perejón, Francisco
Civit Balcells, Antón
Domínguez Morales, Manuel Jesús
author2_role author
author
author
dc.contributor.none.fl_str_mv Arquitectura y Tecnología de Computadores
TEP108: Robótica y Tecnología de Computadores
Ministerio de Ciencia e Innovación (MICIN). España
dc.subject.none.fl_str_mv Skin
Deep learning
Convolutional neural network
Artificial intelligence
topic Skin
Deep learning
Convolutional neural network
Artificial intelligence
description Skin blemishes can be caused by multiple events or diseases and, in some cases, it is difficult to distinguish where they come from. Therefore, there may be cases with a dangerous origin that go unnoticed or the opposite case (which can lead to overcrowding of health services). To avoid this, the use of artificial intelligence-based classifiers using images taken with mobile devices is proposed; this would help in the initial screening process and provide some information to the patient prior to their final diagnosis. To this end, this work proposes an optimization mechanism based on two phases in which a global search for the best classifiers (from among more than 150 combinations) is carried out, and, in the second phase, the best candidates are subjected to a phase of evaluation of the robustness of the system by applying the cross-validation technique. The results obtained reach 99.95% accuracy for the best case and 99.75% AUC. Comparing the developed classifier with previous works, an improvement in terms of classification rate is appreciated, as well as in the reduction of the classifier complexity, which allows our classifier to be integrated in a specific purpose system with few computational resources.
publishDate 2024
dc.date.none.fl_str_mv 2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv https://hdl.handle.net/11441/160923
https://doi.org/10.1016/j.heliyon.2024.e28058
url https://hdl.handle.net/11441/160923
https://doi.org/10.1016/j.heliyon.2024.e28058
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv PID2019-105556GB-C33
https://www.sciencedirect.com/science/article/pii/S2405844024040891?via%3Dihub
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:idUS. Depósito de Investigación de la Universidad de Sevilla
instname:Universidad de Sevilla (US)
instname_str Universidad de Sevilla (US)
reponame_str idUS. Depósito de Investigación de la Universidad de Sevilla
collection idUS. Depósito de Investigación de la Universidad de Sevilla
repository.name.fl_str_mv
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