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Deep Learning and Localized Features...

Missouri University of Science and Technology., Computer Engineering.

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Deep Learning and Localized Features Fusion for Medical Image Classification.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Deep Learning and Localized Features Fusion for Medical Image Classification./
作者:	AlMubarak, Haidar Ali.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	110 p.
附註:	Source: Dissertation Abstracts International, Volume: 79-11(E), Section: B.
Contained By:	Dissertation Abstracts International79-11B(E).
標題:	Computer engineering. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10743608
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. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 110 p.

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

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

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.

ISBN: 9780438111653Subjects--Topical Terms:

621879
Computer engineering.

Deep Learning and Localized Features Fusion for Medical Image Classification.
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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.
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