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Characterization of secondary signals in mammographic images by artificial neural networks to aid diagnosis of breast cancer

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Renan Caldeira Menechelli
Total Authors: 1
Document type: Master's Dissertation
Press: São Carlos.
Institution: Universidade de São Paulo (USP). Escola de Engenharia de São Carlos (EESC/SBD)
Defense date:
Examining board members:
Homero Schiabel; Simone Elias Martinelli; Agma Juci Machado Traina
Advisor: Homero Schiabel

The increase in the number of cases of breast cancer have attracted interest in developing modules of CAD schemes to provider higher diagnostic accuracy. However, most researches are engaged in detect and classify primary factors present in mammographic images such as nodules and microcalcifications. Asymmetric areas, nipple retraction, axilary limph nodes, among other, are considered as secondary factors to diagnostic the breast cancer, although they may alert for the emergence not only of this but of other diseases in the future. Thus, this research includes the implementation of a computer system able to assist in the detection and classification, according to BI-RADS®, of regions that containing secondary signals able to arousing suspicion of the presence or appearance of breast cancer in digital mammographic images using intelligent and automatic techniques in the image processing and artificial neural networks. The accuracy obtained in each step was: detection of asymmetry of 82.8%, nipple retraction of 95% and Az = 0.93, detection of axilary lymph nodes = 74.9%. The purpose is that the result of the work is entered as one of the modules of a prototype of CADx schem in mammography in order to extend the range of information to be used in the classification of each case under analysis, aiming to increase diagnostic accuracy. (AU)

FAPESP's process: 10/13909-9 - Software for aiding asymmetries detection and classification on digital mammography images
Grantee:Renan Caldeira Menechelli
Support Opportunities: Scholarships in Brazil - Master