Docusse, Tiago A. [UNESP]Silva, Alexandre C. R. da [UNESP]Pereira, Aledir S.Marranghello, Norian2015-03-182015-03-182013-01-012013 Aasri Conference On Intelligent Systems And Control. Amsterdam: Elsevier Science Bv, v. 4, p. 90-95, 2013.2212-6716http://hdl.handle.net/11449/116391This paper presents a Computer Aided Diagnosis (CAD) system that automatically classifies microcalcifications detected on digital mammograms into one of the five types proposed by Michele Le Gal, a classification scheme that allows radiologists to determine whether a breast tumor is malignant or not without the need for surgeries. The developed system uses a combination of wavelets and Artificial Neural Networks (ANN) and is executed on an Altera DE2-115 Development Kit, a kit containing a Field-Programmable Gate Array (FPGA) that allows the system to be smaller, cheaper and more energy efficient. Results have shown that the system was able to correctly classify 96.67% of test samples, which can be used as a second opinion by radiologists in breast cancer early diagnosis. (C) 2013 The Authors. Published by Elsevier B.V.90-95engImage classificationCancer detectionMammographyEmbedded softwareTrabalho apresentado em evento10.1016/j.aasri.2013.10.015WOS:000345453000014Acesso abertoWOS000345453000014.pdf20986232628927190000-0003-1086-33120000-0003-1086-3312