Automated Feature Set Selection and Its Application to MCC Identification in Digital Mammograms for Breast Cancer Detection

We propose a fully automated algorithm that is able to select a discriminative feature set from a training database via sequential forward selection (SFS), sequential backward selection (SBS), and F-score methods. We applied this scheme to microcalcifications cluster (MCC) detection in digital mammo...

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Detalles Bibliográficos
Autores principales: Huang, Yi-Jhe, Chan, Ding-Yuan, Cheng, Da-Chuan, Ho, Yung-Jen, Tsai, Po-Pang, Shen, Wu-Chung, Chen, Rui-Fen
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Molecular Diversity Preservation International (MDPI) 2013
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673115/
https://www.ncbi.nlm.nih.gov/pubmed/23580053
http://dx.doi.org/10.3390/s130404855

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3673115/
https://www.ncbi.nlm.nih.gov/pubmed/23580053
http://dx.doi.org/10.3390/s130404855

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