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, 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.
De La Salle University, Manila, Philippines. melvin.cabatuan@dlsu.edu.ph
Melvin K
MK
Cabatuan
Elmer P
EP
Dadios
Raouf N G
RN
Naguib
Andreas
A
Oikonomou
D001185:Artificial Intelligence; D016504:Breast Self-Examination; D005260:Female; D006801:Humans; D016571:Neural Networks, Computer; D010353:Patient Education as Topic