Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms

College

Gokongwei College of Engineering

Department/Unit

Electronics And Communications Engg

Document Type

Conference Proceeding

Source Title

Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS

First Page

6259

Last Page

6262

Publication Date

12-14-2012

Abstract

This paper focuses on breast self-examination (BSE) stroke position and palpation level classification for the development of a computer vision-based BSE training and guidance system. In this study, image frames are extracted from a BSE video and processed considering the color information, shape, and texture by wavelet transform and first order color moment. The new approach using artificial neural network and wavelet transform can identify BSE stroke positions and palpation levels, i.e. light, medium, and deep, at 97.8 % and 87.5 % accuracy respectively. © 2012 IEEE.

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Digitial Object Identifier (DOI)

10.1109/EMBC.2012.6347425

Disciplines

Electrical and Computer Engineering

Keywords

Breast—Examination; Self-examination, Medical; Computer vision in medicine

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