Monocular depth level estimation for breast self-examination (BSE) using RGBD BSE dataset

• Main Authors: Jose, John Anthony C., Cabatuan, Melvin K., Billones, Robert Kerwin, Dadios, Elmer P., Gan Lim, Laurence A.
• Format: text
• Published: Animo Repository 2016
• Subjects: Breast—Examination; Self-examination, Medical; Computer vision in medicine; Depth perception; Biomedical; Electrical and Computer Engineering
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/2387; https://animorepository.dlsu.edu.ph/context/faculty_research/article/3386/type/native/viewcontent
• Institution: De La Salle University

Up until now, there had been no existing literature in depth level estimation algorithm for BSE using a simple camera that provides quantitative accuracy. They can only show their effectiveness thru graphs. In this paper, we present the RGBD BSE dataset and a depth level quantization scheme that provides an avenue for training a Machine learning model and calculating its hit rate. We were able to show that the previous study's accuracy is 30.33%. Moreover, adding a simple shadow area as feature and changing the Machine Learning prediction model to Support Vector Machine boosts the algorithm's accuracy to 58.83%. © 2015 IEEE.