Kleyko, D. and Khan, S. and Osipov, E. and Yong, S.-P. (2017) Modality classification of medical images with distributed representations based on cellular automata reservoir computing. Proceedings - International Symposium on Biomedical Imaging. pp. 1053-1056.
Full text not available from this repository.Abstract
Modality corresponding to medical images is a vital filter in medical image retrieval systems. This article presents the classification of modalities of medical images based on the usage of principles of hyper-dimensional computing and reservoir computing. It is demonstrated that the highest classification accuracy of the proposed method is on a par with the best classical method for the given dataset (83 vs. 84). The major positive property of the proposed method is that it does not require any optimization routine during the training phase and naturally allows for incremental learning upon the availability of new training data. © 2017 IEEE.
Item Type: | Article |
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Impact Factor: | cited By 0 |
Departments / MOR / COE: | Division > Academic > Faculty of Science & Information Technology > Computer Information Sciences |
Depositing User: | Mr Ahmad Suhairi Mohamed Lazim |
Date Deposited: | 22 Apr 2018 14:39 |
Last Modified: | 22 Apr 2018 14:39 |
URI: | http://scholars.utp.edu.my/id/eprint/20071 |