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dc.contributor | Escuela Superior y Tecnica de Ingenieros de Minas | es_ES |
dc.contributor.author | Courtenay, Lloyd A. | |
dc.contributor.author | González Aguilera, Diego | |
dc.contributor.author | Lagüela López, Susana | |
dc.contributor.author | Del Pozo, Susana | |
dc.contributor.author | Ruiz Mendez, Camilo | |
dc.contributor.author | Barbero García, Inés | |
dc.contributor.author | Román Curto, Concepción | |
dc.contributor.author | Cañueto, Javier | |
dc.contributor.author | Santos Durán, Carlos | |
dc.contributor.author | Cardeñoso Álvarez, María Esther | |
dc.contributor.author | Roncero Riesco, Mónica | |
dc.contributor.author | Hernández López, David | |
dc.contributor.author | Guerrero Sevilla, Diego | |
dc.contributor.author | Rodríguez Gonzálvez, Pablo | |
dc.contributor.other | Ingeniería Cartografica, Geodesica y Fotogrametria | es_ES |
dc.date | 2022-04-19 | |
dc.date.accessioned | 2024-01-08T11:37:26Z | |
dc.date.available | 2024-01-08T11:37:26Z | |
dc.identifier.citation | Courtenay, L. A., González-Aguilera, D., Lagüela, S., Pozo, S. D., Ruiz, C., Barbero-García, I., Román Curto, C., Cañueto, J., Santos Durán, C., Cardeñoso Álvarez, M.E., Roncero Riesco, M., Hernández López, D., Guerrero Sevilla, D., Rodríguez-Gonzalvez, P. (2022). Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures. Journal of Clinical Medicine, 11(9), 1-13.https://doi.org/10.3390/jcm11092315 | es_ES |
dc.identifier.uri | https://hdl.handle.net/10612/17540 | |
dc.description.abstract | [EN] Non-melanoma skin cancer, and basal cell carcinoma in particular, is one of the most common types of cancer. Although this type of malignancy has lower metastatic rates than other types of skin cancer, its locally destructive nature and the advantages of its timely treatment make early detection vital. The combination of multispectral imaging and artificial intelligence has arisen as a powerful tool for the detection and classification of skin cancer in a non-invasive manner. The present study uses hyperspectral images to discern between healthy and basal cell carcinoma hyperspectral signatures. Upon the combined use of convolutional neural networks, with a final support vector machine activation layer, the present study reaches up to 90% accuracy, with an area under the receiver operating characteristic curve being calculated at 0.9 as well. While the results are promising, future research should build upon a dataset with a larger number of patients. | es_ES |
dc.language | eng | es_ES |
dc.publisher | MDPI | es_ES |
dc.rights | Attribution-NonCommercial-NoDerivatives 4.0 Internacional | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | * |
dc.subject | Ingenierías | es_ES |
dc.subject.other | Basal cell carcinoma | es_ES |
dc.subject.other | Hyperspectral sensor | es_ES |
dc.subject.other | Computational learning | es_ES |
dc.subject.other | Convolutional neural networks | es_ES |
dc.subject.other | Support vector machines | es_ES |
dc.title | Deep Convolutional Neural Support Vector Machines for the Classification of Basal Cell Carcinoma Hyperspectral Signatures | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.identifier.doi | 10.3390/jcm11092315 | |
dc.description.peerreviewed | SI | es_ES |
dc.relation.projectID | info:eu-repo/grantAgreement/JCL//GRS 1837/A/18/HYPER-SKINCARE | es_ES |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | es_ES |
dc.identifier.essn | 2077-0383 | |
dc.journal.title | Journal of clinical medicine | es_ES |
dc.volume.number | 11 | es_ES |
dc.issue.number | 9 | es_ES |
dc.page.initial | 1 | es_ES |
dc.page.final | 13 | es_ES |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |
dc.description.project | Junta de Castilla y Leon | es_ES |
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