Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions
Oral cancer is a frequently malignant tumor that can be detected during an oral examination. Unfortunately, it is often diagnosed in advanced stages, which leads to low survival rates of about 50% at five years. Due to the low survival rate, it is crucial to develop automated systems that allow the...
| Autores: | , , , , , , , , , , , , , |
|---|---|
| Formato: | artículo |
| Fecha de publicación: | 2025 |
| País: | España |
| Recursos: | Universidad Nacional de Educación a Distancia |
| Repositorio: | e-spacio. Repositorio Institucional de la UNED |
| Idioma: | inglés |
| OAI Identifier: | oai:e-spacio.uned.es:20.500.14468/31698 |
| Acesso em linha: | https://hdl.handle.net/20.500.14468/31698 |
| Access Level: | acceso abierto |
| Palavra-chave: | 1203.17 Informática images classification oral cancer oral potentially malignant disorders deep learning convolutional neural network skip connection networks visual transformers convNeXt ODS 3 - Salud y bienestar ODS 9 - Industria, innovación e infraestructura |
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Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictionsRedondo, AlejandroIvaylova, KaterinaBachiller Mayoral, MargaritaRincón Zamorano, MarianoCuadra Troncoso, José ManuelTamimi, FalehLópez Cedrún, José LuisDiniz Freitas, MárcioLago Méndez, LucíaRubín Roger, GuillermoTorres, JesúsBagán, LeticiaHernández, GonzaloLópez-Pintor, Rosa María1203.17 Informáticaimages classificationoral canceroral potentially malignant disordersdeep learningconvolutional neural networkskip connection networksvisual transformersconvNeXtODS 3 - Salud y bienestarODS 9 - Industria, innovación e infraestructuraOral cancer is a frequently malignant tumor that can be detected during an oral examination. Unfortunately, it is often diagnosed in advanced stages, which leads to low survival rates of about 50% at five years. Due to the low survival rate, it is crucial to develop automated systems that allow the classification of oral lesions according to their severity, aiding in the early diagnosis of oral cancer. This study aims to investigate the effectiveness of using clinical images and deep learning based models to perform a multiclass classification of oral mucosal lesions in color photographs taken without following any acquisition protocol. The classification differentiated four classes: malignant, potentially malignant, benign and healthy. The dataset included a total of 3246 images from 1013 patients, with 40 different categories of oral lesions, including healthy oral mucosa. The images showed different areas of the oral cavity and were captured from different perspectives by diverse dentists and maxillofacial surgeons in the practice. For the classification, different deep learning architectures were applied and compared, from the best known convolutional neural networks (CNN) and skip connection networks (SCN), to more innovative architectures such as visual transformers and a recent hybrid architecture, ConvNeXt v2. The ConvNeXt v2 Tiny architecture, with 85.53% accuracy, 85.02% precision, 85.50% recall, 84.92% F1-score, and 97.40% ROC AUC for an input image size of 354 × 354 pixels, outperformed the other architectures on the same database. The present model improved on previous proposals by considering a greater number of oral lesions and output classes.ELSEVIERInstituto de Salud Carlos III (ISCIII)Agencia Estatal de Investigacióne-Spacio UNED20262026-02-0420252025-07-1920252025-07-19journal articlehttp://purl.org/coar/resource_type/c_6501info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/20.500.14468/31698reponame:e-spacio. Repositorio Institucional de la UNEDinstname:Universidad Nacional de Educación a DistanciaInglésengopen accesshttp://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.esoai:e-spacio.uned.es:20.500.14468/316982026-06-06T12:38:31Z |
| dc.title.none.fl_str_mv |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| title |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| spellingShingle |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions Redondo, Alejandro 1203.17 Informática images classification oral cancer oral potentially malignant disorders deep learning convolutional neural network skip connection networks visual transformers convNeXt ODS 3 - Salud y bienestar ODS 9 - Industria, innovación e infraestructura |
| title_short |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| title_full |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| title_fullStr |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| title_full_unstemmed |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| title_sort |
Multiclass classification of oral mucosal lesions by deep learning from clinical images without performing any restrictions |
| dc.creator.none.fl_str_mv |
Redondo, Alejandro Ivaylova, Katerina Bachiller Mayoral, Margarita Rincón Zamorano, Mariano Cuadra Troncoso, José Manuel Tamimi, Faleh López Cedrún, José Luis Diniz Freitas, Márcio Lago Méndez, Lucía Rubín Roger, Guillermo Torres, Jesús Bagán, Leticia Hernández, Gonzalo López-Pintor, Rosa María |
| author |
Redondo, Alejandro |
| author_facet |
Redondo, Alejandro Ivaylova, Katerina Bachiller Mayoral, Margarita Rincón Zamorano, Mariano Cuadra Troncoso, José Manuel Tamimi, Faleh López Cedrún, José Luis Diniz Freitas, Márcio Lago Méndez, Lucía Rubín Roger, Guillermo Torres, Jesús Bagán, Leticia Hernández, Gonzalo López-Pintor, Rosa María |
| author_role |
author |
| author2 |
Ivaylova, Katerina Bachiller Mayoral, Margarita Rincón Zamorano, Mariano Cuadra Troncoso, José Manuel Tamimi, Faleh López Cedrún, José Luis Diniz Freitas, Márcio Lago Méndez, Lucía Rubín Roger, Guillermo Torres, Jesús Bagán, Leticia Hernández, Gonzalo López-Pintor, Rosa María |
| author2_role |
author author author author author author author author author author author author author |
| dc.contributor.none.fl_str_mv |
Instituto de Salud Carlos III (ISCIII) Agencia Estatal de Investigación e-Spacio UNED |
| dc.subject.none.fl_str_mv |
1203.17 Informática images classification oral cancer oral potentially malignant disorders deep learning convolutional neural network skip connection networks visual transformers convNeXt ODS 3 - Salud y bienestar ODS 9 - Industria, innovación e infraestructura |
| topic |
1203.17 Informática images classification oral cancer oral potentially malignant disorders deep learning convolutional neural network skip connection networks visual transformers convNeXt ODS 3 - Salud y bienestar ODS 9 - Industria, innovación e infraestructura |
| description |
Oral cancer is a frequently malignant tumor that can be detected during an oral examination. Unfortunately, it is often diagnosed in advanced stages, which leads to low survival rates of about 50% at five years. Due to the low survival rate, it is crucial to develop automated systems that allow the classification of oral lesions according to their severity, aiding in the early diagnosis of oral cancer. This study aims to investigate the effectiveness of using clinical images and deep learning based models to perform a multiclass classification of oral mucosal lesions in color photographs taken without following any acquisition protocol. The classification differentiated four classes: malignant, potentially malignant, benign and healthy. The dataset included a total of 3246 images from 1013 patients, with 40 different categories of oral lesions, including healthy oral mucosa. The images showed different areas of the oral cavity and were captured from different perspectives by diverse dentists and maxillofacial surgeons in the practice. For the classification, different deep learning architectures were applied and compared, from the best known convolutional neural networks (CNN) and skip connection networks (SCN), to more innovative architectures such as visual transformers and a recent hybrid architecture, ConvNeXt v2. The ConvNeXt v2 Tiny architecture, with 85.53% accuracy, 85.02% precision, 85.50% recall, 84.92% F1-score, and 97.40% ROC AUC for an input image size of 354 × 354 pixels, outperformed the other architectures on the same database. The present model improved on previous proposals by considering a greater number of oral lesions and output classes. |
| publishDate |
2025 |
| dc.date.none.fl_str_mv |
2025 2025-07-19 2025 2025-07-19 2026 2026-02-04 |
| dc.type.none.fl_str_mv |
journal article http://purl.org/coar/resource_type/c_6501 |
| dc.type.openaire.fl_str_mv |
info:eu-repo/semantics/article |
| format |
article |
| dc.identifier.none.fl_str_mv |
https://hdl.handle.net/20.500.14468/31698 |
| url |
https://hdl.handle.net/20.500.14468/31698 |
| dc.language.none.fl_str_mv |
Inglés eng |
| language_invalid_str_mv |
Inglés |
| language |
eng |
| dc.rights.none.fl_str_mv |
open access http://purl.org/coar/access_right/c_abf2 info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es |
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open access http://purl.org/coar/access_right/c_abf2 http://creativecommons.org/licenses/by-nc-nd/4.0/deed.es |
| eu_rights_str_mv |
openAccess |
| dc.format.none.fl_str_mv |
application/pdf |
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ELSEVIER |
| publisher.none.fl_str_mv |
ELSEVIER |
| dc.source.none.fl_str_mv |
reponame:e-spacio. Repositorio Institucional de la UNED instname:Universidad Nacional de Educación a Distancia |
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Universidad Nacional de Educación a Distancia |
| reponame_str |
e-spacio. Repositorio Institucional de la UNED |
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e-spacio. Repositorio Institucional de la UNED |
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15,198674 |