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

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

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 pro...

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Bibliographic Details
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
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Institution: De La Salle University
Description
Summary: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.

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