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

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

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, a...

Full description

Saved in:
Bibliographic Details
Main Authors: Cabatuan, Melvin K., Dadios, Elmer Jose P., Naguib, Raouf N. G., Oikonomou, Andreas
Format: text
Published: Animo Repository 2012
Subjects:
Breast—Examination
Self-examination, Medical
Computer vision in medicine
Electrical and Computer Engineering
Online Access:https://animorepository.dlsu.edu.ph/faculty_research/563
Tags: Add Tag
No Tags, Be the first to tag this record!
Institution: De La Salle University
id oai:animorepository.dlsu.edu.ph:faculty_research-1562
record_format eprints
spelling oai:animorepository.dlsu.edu.ph:faculty_research-15622022-05-11T03:03:49Z Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms Cabatuan, Melvin K. Dadios, Elmer Jose P. Naguib, Raouf N. G. Oikonomou, Andreas 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. 2012-12-14T08:00:00Z text text/html https://animorepository.dlsu.edu.ph/faculty_research/563 Faculty Research Work Animo Repository Breast—Examination Self-examination, Medical Computer vision in medicine Electrical and Computer Engineering
institution De La Salle University
building De La Salle University Library
continent Asia
country Philippines
Philippines
content_provider De La Salle University Library
collection DLSU Institutional Repository
topic Breast—Examination
Self-examination, Medical
Computer vision in medicine
Electrical and Computer Engineering
spellingShingle Breast—Examination
Self-examination, Medical
Computer vision in medicine
Electrical and Computer Engineering
Cabatuan, Melvin K.
Dadios, Elmer Jose P.
Naguib, Raouf N. G.
Oikonomou, Andreas
Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
description 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.
format text
author Cabatuan, Melvin K.
Dadios, Elmer Jose P.
Naguib, Raouf N. G.
Oikonomou, Andreas
author_facet Cabatuan, Melvin K.
Dadios, Elmer Jose P.
Naguib, Raouf N. G.
Oikonomou, Andreas
author_sort Cabatuan, Melvin K.
title Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
title_short Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
title_full Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
title_fullStr Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
title_full_unstemmed Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
title_sort computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
publisher Animo Repository
publishDate 2012
url https://animorepository.dlsu.edu.ph/faculty_research/563
_version_ 1733052799709609984

Similar Items