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Novel Hybrid CNN-SVM Model for Recognition of Functional Magnetic Resonance Images

Title
Novel Hybrid CNN-SVM Model for Recognition of Functional Magnetic Resonance Images
Author
강경태
Keywords
hybrid model; support vector machine; neural network; functional magnetic resonance imaging recognition
Issue Date
2017-10
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
2017 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Page. 1001-1006
Abstract
This paper proposes a novel hybrid model that integrates the synergy of two superior classifiers for functional magnetic resonance imaging (fMRI) recognition, namely, convolutional neural networks (CNNs) and support vector machines (SVMs), both of which have proven results in the field of image recognition. In the proposed model, the CNN functions as a trainable feature extractor and the SVM functions as a recognizer. This hybrid model extracts features from raw images and generates predictions for fMRI recognition. We conducted experiments on Haxby's 2001 fMRI dataset. Comparisons with Haxby's study using the same database indicated that the proposed fusion achieved superior recognition accuracy of 99.5% compared to the Haxby's approach. Further, when the CNN was used as a feature extractor, the SVM classifier was demonstrated to be the best combining counterpart, providing the best synergy effect in terms of accuracy. This is compared with other classifiers based on learning algorithms such as decision tree, neural network, K-nearest neighbor, random forest, and AdaBoost.
URI
https://ieeexplore.ieee.org/document/8122741/http://repository.hanyang.ac.kr/handle/20.500.11754/103690
ISBN
978-1-5386-1645-1; 978-1-5386-1646-8
DOI
10.1109/SMC.2017.8122741
Appears in Collections:
COLLEGE OF COMPUTING[E] > COMPUTER SCIENCE(소프트웨어학부) > Articles
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