2024-03-29T11:19:16Zhttps://repositori.uji.es/oai/requestoai:repositori.uji.es:10234/832272022-11-08T14:56:54Zcom_10234_43662com_10234_9col_10234_43643
Repositori UJI
author
Quinzán, Ianisse
author
Martínez Sotoca, José
author
Latorre Carmona, Pedro
author
Pla, Filiberto
author
García-Sevilla, Pedro
author
Boldó, Enrique
2014-02-12T10:20:59Z
2014-02-12T10:20:59Z
2013-03-04
QUINZÁN, Ianisse, et al. Band selection in spectral imaging for non-invasive melanoma diagnosis. Biomedical optics express, 2013, vol. 4, no 4, p. 514-519
2156-7085
http://hdl.handle.net/10234/83227
http://dx.doi.org/10.1364/BOE.4.000514
A method consisting of the combination of the Synthetic Minority Over-Sampling TEchnique (SMOTE) and the Sequential Forward Floating Selection (SFFS) technique is used to do band selection in a highly imbalanced, small size, two-class multispectral dataset of melanoma and non-melanoma lesions. The aim is to improve classification rate and help to identify those spectral bands that have a more important role in melanoma detection. All the processing steps were designed taking into account the low number of samples in the dataset, situation that is quite common in medical cases. The training/test sets are built using a Leave-One-Out strategy. SMOTE is applied in order to deal with the imbalance problem, together with the Qualified Majority Voting scheme (QMV). Support Vector Machines (SVM) is the classification method applied over each balanced set. Results indicate that all melanoma lesions are correctly classified, using a low number of bands, reaching 100% sensitivity and 72% specificity when considering nine (out of a total of 55) spectral bands.
eng
© 2013 OSA
Melanoma
Spectral imaging
Diagnosis
Band selection in spectral imaging for non-invasive melanoma diagnosis
info:eu-repo/semantics/article
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