The Resource Pseudo random forests for tube identification, Kendall Lee Bingham

Pseudo random forests for tube identification, Kendall Lee Bingham

Label
Pseudo random forests for tube identification
Title
Pseudo random forests for tube identification
Statement of responsibility
Kendall Lee Bingham
Creator
Contributor
Author
Degree supervisor
Subject
Genre
Language
eng
Summary
Random forests are widely used in machine learning as they can potentially offer higher accuracy than individual decision trees by the averaging of multiple independent models. We propose a modification called 2Pseudo-random forests3 that combines stochastic feature selection with dynamic problem-specific feature generation. As proof of concept, we apply the method to the problem of edge detection and classification in radiographic images. In particular, we use the method to detect feeding tubes in pediatric patients, which are inserted to deliver food and medicine. Since multiple layers of tissues and medical objects are overlaid in a single image, these can be difficult to read on x-rays, even for trained radiologists. The placement of these tubes is critical to the well-being and care of the patient. Automating the recognition of these tubes can help confirm the correct placement of these tubes, as an improperly placed tube could delay treatment or jeopardize the health of the patient. It can also save time by enhancing the visibility of tubes for interpretation by radiologists, as hospitals may have to validate tens to hundreds of these x-rays a day. We report an average recall of 85% for tube pixel identification by using Pseudo-random forests for classification, based on leave-out-one cross-validation. Further improvement is possible by post-processing for tube continuity and the incorporation of other techniques developed as part of the research
Cataloging source
UMK
http://library.link/vocab/creatorDate
1969-
http://library.link/vocab/creatorName
Bingham, Kendall Lee
Degree
M.S.
Dissertation note
School of Computing and Engineering.
Dissertation year
2015.
Granting institution
University of Missouri-Kansas City,
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorName
Dinakarpandian, Deendayal
http://library.link/vocab/subjectName
  • Decision trees
  • Trees (Graph theory)
  • Radiography
  • Tube feeding
Label
Pseudo random forests for tube identification, Kendall Lee Bingham
Instantiates
Publication
Note
  • "A thesis in Computer Science."
  • Advisor: Deendayal Dinakarpandian
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 52-53)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Methodology -- Evaluation -- Conclusions and future work
Control code
945369303
Dimensions
unknown
Extent
1 online resource (54 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)945369303
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web
Label
Pseudo random forests for tube identification, Kendall Lee Bingham
Publication
Note
  • "A thesis in Computer Science."
  • Advisor: Deendayal Dinakarpandian
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 52-53)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Methodology -- Evaluation -- Conclusions and future work
Control code
945369303
Dimensions
unknown
Extent
1 online resource (54 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)945369303
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web

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