CORE
🇺🇦
make metadata, not war
Services
Services overview
Explore all CORE services
Access to raw data
API
Dataset
FastSync
Content discovery
Recommender
Discovery
OAI identifiers
OAI Resolver
Managing content
Dashboard
Bespoke contracts
Consultancy services
Support us
Support us
Membership
Sponsorship
Community governance
Advisory Board
Board of supporters
Research network
About
About us
Our mission
Team
Blog
FAQs
Contact us
Computer vision-based breast self-examination stroke position and palpation pressure level classification using artificial neural networks and wavelet transforms
Authors
Melvin K. Cabatuan
Elmer P. Dadios
Raouf N. G. Naguib
Andreas Oikonomou
Publication date
14 December 2012
Publisher
Animo Repository
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
Similar works
Full text
Available Versions
Animo Repository - De La Salle University Research
See this paper in CORE
Go to the repository landing page
Download from data provider
oai:animorepository.dlsu.edu.p...
Last time updated on 03/12/2021