Análise e comparação de algoritmos de classificação para o diagnóstico de câncer de mama
Abstract
The medical diagnosis based on images presents several computational challenges, including the phases of acquisition, pre-processing, segmentation and classification of the images. This work starts from the analysis of a database of magnetic resonance images of breast tumors (mammograms) and implementation of machine learning algorithms for the comparison of the best classifying model for the diagnosis of breast cancer.
References
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INCA 2019, INSTITUTO NACIONAL DE CÂNCER. Disponível em: https://www.inca.gov.br/tipos-de-cancer/cancer-de-mama
FLORIDA, U. of S. DDSM: Digital Database for Screening Mammography. Disponível em: http://www.eng.usf.edu/cvprg/Mammography/Database.html.
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OJALA, M. P. T.; HARWOOD, D. A comparative study of texture measures with classification based on feature distributions. Pattern Recoonition, Vol. 29, No. l, pp. 51 59, 1996., 1996.
INCA 2019, INSTITUTO NACIONAL DE CÂNCER. Disponível em: https://www.inca.gov.br/tipos-de-cancer/cancer-de-mama
FLORIDA, U. of S. DDSM: Digital Database for Screening Mammography. Disponível em: http://www.eng.usf.edu/cvprg/Mammography/Database.html.
SBM 2019, SOCIEDADE BRASILEIRA DE MASTOLOGIA. Disponível em: http://www.sbmastologia.com.br/
HARALICK, R. M. Statistical and structural approaches to texture. Proc. IEEE, vol. 67, no. 5, pp. 786-804, 1979., 1979.
HERBRICH, G. Machine Learning: An Algorithmic Perspective. second edition. [S.l.]: Chapman & Hall/CRC, 2015.
OJALA, M. P. T.; HARWOOD, D. A comparative study of texture measures with classification based on feature distributions. Pattern Recoonition, Vol. 29, No. l, pp. 51 59, 1996., 1996.
Published
2021-10-25
How to Cite
SILVA, Jonas F.; NEVES, Tácito.
Análise e comparação de algoritmos de classificação para o diagnóstico de câncer de mama. In: REGIONAL SCHOOL ON COMPUTING OF BAHIA, ALAGOAS, AND SERGIPE (ERBASE), 21. , 2021, Maceió.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2021
.
p. 74-80.
DOI: https://doi.org/10.5753/erbase.2021.20059.
