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Deep Learning Method vs. Hand-Crafte...

ProQuest Information and Learning Co.

Deep Learning Method vs. Hand-Crafted Features for Lung Cancer Diagnosis and Breast Cancer Risk Analysis.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Deep Learning Method vs. Hand-Crafted Features for Lung Cancer Diagnosis and Breast Cancer Risk Analysis./
Author:	Sun, Wenqing.
Description:	1 online resource (74 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9781369886900

Deep Learning Method vs. Hand-Crafted Features for Lung Cancer Diagnosis and Breast Cancer Risk Analysis.
Sun, Wenqing.

Deep Learning Method vs. Hand-Crafted Features for Lung Cancer Diagnosis and Breast Cancer Risk Analysis. - 1 online resource (74 pages)

Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.

Thesis (Ph.D.)--The University of Texas at El Paso, 2017.

Includes bibliographical references

Breast cancer and lung cancer are two major leading causes of cancer deaths, and researchers have been developing computer aided diagnosis (CAD) system to automatically diagnose them for decades. In recent studies, we found that the techniques in CAD system can also be used for breast cancer risk analysis, like feature design and machine learning. Also we noticed that with the development of deep learning methods, the performance of CAD system can be improved by using computer automatically generated features. To explore these possibilities, we conducted a series of studies: the first two studies focused on transferring the original CAD system techniques to breast cancer risk analysis models; and the next two studies compared the performance of our proposed schemes using deep learning methods and traditional methods on breast cancer risk analysis and lung cancer diagnosis.

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

Mode of access: World Wide Web

ISBN: 9781369886900Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Deep Learning Method vs. Hand-Crafted Features for Lung Cancer Diagnosis and Breast Cancer Risk Analysis.
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100 1 $a Sun, Wenqing. $3 1188612
245 1 0 $a Deep Learning Method vs. Hand-Crafted Features for Lung Cancer Diagnosis and Breast Cancer Risk Analysis.
264 0 $c 2017
300 $a 1 online resource (74 pages)
336 $a text $b txt $2 rdacontent
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500 $a Source: Dissertation Abstracts International, Volume: 78-11(E), Section: B.
500 $a Advisers: Wei Qian; Bill (Tzu-Liang) Tseng.
502 $a Thesis (Ph.D.)--The University of Texas at El Paso, 2017.
504 $a Includes bibliographical references
520 $a Breast cancer and lung cancer are two major leading causes of cancer deaths, and researchers have been developing computer aided diagnosis (CAD) system to automatically diagnose them for decades. In recent studies, we found that the techniques in CAD system can also be used for breast cancer risk analysis, like feature design and machine learning. Also we noticed that with the development of deep learning methods, the performance of CAD system can be improved by using computer automatically generated features. To explore these possibilities, we conducted a series of studies: the first two studies focused on transferring the original CAD system techniques to breast cancer risk analysis models; and the next two studies compared the performance of our proposed schemes using deep learning methods and traditional methods on breast cancer risk analysis and lung cancer diagnosis.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Medical imaging. $3 1180167
650 4 $a Oncology. $3 593951
650 4 $a Artificial intelligence. $3 559380
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0574
690 $a 0992
690 $a 0800
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a The University of Texas at El Paso. $b Electrical Eng.. $3 1186964
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10271382 $z click for full text (PQDT)

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