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A wavelet deep belief network-based classifier for medical images
conference contribution
posted on 2016-01-01, 00:00 authored by Seyedamin Khatami, Abbas KhosraviAbbas Khosravi, Chee Peng LimChee Peng Lim, Saeid NahavandiAccurately and quickly classifying high dimensional data using machine learning and data mining techniques is problematic and challenging. This paper proposes an efficient and effective technique to properly extract high level features from medical images using a deep network and precisely classify them using support vector machine. A wavelet filter is applied at the first step of the proposed method to obtain the informative coefficient matrix of each image and to reduce dimensionality of feature space. A four-layer deep belief network is also utilized to extract high level features. These features are then fed to a support vector machine to perform accurate classification. Comparative empirical results demonstrate the strength, precision, and fast-response of the proposed technique.
History
Event
Neural Information Processing. International Conference (23rd : 2016 : Kyoto, Japan)Volume
9949Issue
Part 3Series
Lecture Notes in Computer SciencePagination
467 - 474Publisher
SpringerLocation
Kyoto, JapanPlace of publication
Cham, SwitzerlandPublisher DOI
Start date
2016-10-16End date
2016-10-21ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319466743Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2016, Springer International Publishing AGTitle of proceedings
ICONIP 2016: Proceedings of the 23rd International Conference on Neural Information ProcessingUsage metrics
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