Modality classification of medical images with distributed representations based on cellular automata reservoir computing

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 classif...

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Main Authors: Kleyko, D., Khan, S., Osipov, E., Yong, S.-P.
Format: Article
Institution: Universiti Teknologi Petronas
Record Id / ISBN-0: utp-eprints.20071 /
Published: IEEE Computer Society 2017
Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023198723&doi=10.1109%2fISBI.2017.7950697&partnerID=40&md5=94f3e21c26cfd8119076001e82236597
http://eprints.utp.edu.my/20071/
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id utp-eprints.20071
recordtype eprints
spelling utp-eprints.200712018-04-22T14:39:36Z Modality classification of medical images with distributed representations based on cellular automata reservoir computing Kleyko, D. Khan, S. Osipov, E. Yong, S.-P. 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. IEEE Computer Society 2017 Article PeerReviewed https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023198723&doi=10.1109%2fISBI.2017.7950697&partnerID=40&md5=94f3e21c26cfd8119076001e82236597 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. http://eprints.utp.edu.my/20071/
institution Universiti Teknologi Petronas
collection UTP Institutional Repository
description 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.
format Article
author Kleyko, D.
Khan, S.
Osipov, E.
Yong, S.-P.
spellingShingle Kleyko, D.
Khan, S.
Osipov, E.
Yong, S.-P.
Modality classification of medical images with distributed representations based on cellular automata reservoir computing
author_sort Kleyko, D.
title Modality classification of medical images with distributed representations based on cellular automata reservoir computing
title_short Modality classification of medical images with distributed representations based on cellular automata reservoir computing
title_full Modality classification of medical images with distributed representations based on cellular automata reservoir computing
title_fullStr Modality classification of medical images with distributed representations based on cellular automata reservoir computing
title_full_unstemmed Modality classification of medical images with distributed representations based on cellular automata reservoir computing
title_sort modality classification of medical images with distributed representations based on cellular automata reservoir computing
publisher IEEE Computer Society
publishDate 2017
url https://www.scopus.com/inward/record.uri?eid=2-s2.0-85023198723&doi=10.1109%2fISBI.2017.7950697&partnerID=40&md5=94f3e21c26cfd8119076001e82236597
http://eprints.utp.edu.my/20071/
_version_ 1741196312418713600
score 11.62408

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