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Deep Learning and Localized Features...

ProQuest Information and Learning Co.

Deep Learning and Localized Features Fusion for Medical Image Classification.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning and Localized Features Fusion for Medical Image Classification./
Author:	AlMubarak, Haidar Ali.
Description:	1 online resource (110 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
Subject:	Computer engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780438111653

Deep Learning and Localized Features Fusion for Medical Image Classification.
AlMubarak, Haidar Ali.

Deep Learning and Localized Features Fusion for Medical Image Classification. - 1 online resource (110 pages)

Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.

Thesis (Ph.D.)--Missouri University of Science and Technology, 2018.

Includes bibliographical references

Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780438111653Subjects--Topical Terms:

569006
Computer engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning and Localized Features Fusion for Medical Image Classification.
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020 $a 9780438111653
035 $a (MiAaPQ)AAI10743608
035 $a (MiAaPQ)missouriscitech:11143
035 $a AAI10743608
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a AlMubarak, Haidar Ali. $3 1190711
245 1 0 $a Deep Learning and Localized Features Fusion for Medical Image Classification.
264 0 $c 2018
300 $a 1 online resource (110 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
500 $a Adviser: Ronald J. Stanley.
502 $a Thesis (Ph.D.)--Missouri University of Science and Technology, 2018.
504 $a Includes bibliographical references
520 $a Local image features play an important role in many classification tasks as translation and rotation do not severely deteriorate the classification process. They have been commonly used for medical image analysis. In medical applications, it is important to get accurate diagnosis/aid results in the fastest time possible.
520 $a This dissertation tries to tackle these problems, first by developing a localized feature-based classification system for medical images and using these features and to give a classification for the entire image, and second, by improving the computational complexity of feature analysis to make it viable as a diagnostic aid system in practical clinical situations.
520 $a For local feature development, a new approach based on combining the rising deep learning paradigm with the use of handcrafted features is developed to classify cervical tissue histology images into different cervical intra-epithelial neoplasia classes. Using deep learning combined with handcrafted features improved the accuracy by 8.4% achieving 80.72% exact class classification accuracy compared to 72.29% when using the benchmark feature-based classification method.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer engineering. $3 569006
650 4 $a Medical imaging. $3 1180167
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0464
690 $a 0574
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a Missouri University of Science and Technology. $b Computer Engineering. $3 1190712
773 0 $t Dissertation Abstracts International $g 79-11B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743608 $z click for full text (PQDT)

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