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Author Eman, M.N. ♦ Cabatuan, M.K. ♦ Dadios, E.P. ♦ Gan Lim, L.A.
Source IEEE Xplore Digital Library
Content type Text
Publisher Institute of Electrical and Electronics Engineers, Inc. (IEEE)
File Format PDF
Copyright Year ©2013
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Artificial neural networks ♦ Training ♦ Breast cancer ♦ Image color analysis ♦ Feature extraction ♦ neural network ♦ breast cancer self-examination ♦ breast detection
Abstract The general aim of this research is helping women to perform breast self-examination (BSE) for finding out any abnormality, change, or lump in the breasts. BSE involves checking the breasts for finding abnormalities, lumps, or changes. This paper reports about our initial efforts to detect and track the left and right breasts in real-time imaging. Image frames were processed considering the color information, and integral image processing to segment regions of interest (ROI) according to common colors of breast features. After getting the preliminary candidate regions, the vector of features were used as the inputs of neural network. The algorithm applies each ROI into the artificial neural network (ANN) for detection of the right and left breasts. Results of the study show that the proposed ANN successfully identifies the position and location of the breasts.
Description Author affiliation: Dept. of Electron. & Commun. Eng., De La Salle Univ., Manila, Philippines (Eman, M.N.; Cabatuan, M.K.) || Dept. of Manuf. Eng. & Manage., De La Salle Univ., Manila, Philippines (Dadios, E.P.) || Dept. of Mech. Eng., De La Salle Univ., Manila, Philippines (Gan Lim, L.A.)
ISBN 9781479928255
ISSN 21593450
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research ♦ Reading
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2013-10-22
Publisher Place China
Rights Holder Institute of Electrical and Electronics Engineers, Inc. (IEEE)
e-ISBN 9781479928279
Size (in Bytes) 811.69 kB
Page Count 4
Starting Page 1
Ending Page 4

Source: IEEE Xplore Digital Library