Deep Learning Methods for Visual Fault Diagnostics of Dental X-ray Systems
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Perustieteiden korkeakoulu |
Master's thesis
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Authors
Date
2018-08-20
Department
Major/Subject
Biomedical Engineering
Mcode
SCI3059
Degree programme
Master’s Programme in Life Science Technologies
Language
en
Pages
64
Series
Abstract
Dental X-ray systems go through rigorous quality assurance protocols following their production and assembly. The protocols include tests, which address the image quality and find certain errors or artifacts that may be present in the images. Detecting faults from the images require human effort, experience, and time. Recent advances in deep learning have proven them to be successful in image classification, object detection, machine translation. The applications of deep learning can be extended to fault detection in X-ray systems. This thesis work consists of surveying, applying, and developing state-of-art deep learning approaches for detection of visual faults or artifacts in the dental X-ray systems. In this thesis, we have shown that deep learning methods can detect geometry and collimator artifacts from X-ray images efficiently and rapidly. This thesis is a precursor for further development of deep learning methods to include detection of wide range of faults and artifacts in X-ray systems to ease quality assurance, calibration, and device maintenance.Description
Supervisor
Särkkä, SimoThesis advisor
Karhu, KalleKeywords
X ray, CBCT imaging, deep learning, visual faults diagnostics, convolutional neural networks