Beltran Perez, C. and Wei, H.-L. orcid.org/0000-0002-4704-7346 (2018) Image classification using generalized multiscale RBF networks and discrete cosine transform. In: 2018 24th International Conference on Automation and Computing (ICAC). 24th International Conference on Automation and Computing (ICAC), 06-07 Sep 2018, Newcastle upon Tyne, UK. IEEE ISBN 9781862203419
Abstract
The use of the multiscale generalized radial basis function (MSRBF) network for image feature extraction is proposed for the first time. The MSRBF network holds a simple but flexible structure capable to modelling complex systems. However MSRBF is originally designed to identify observational-type input-output systems. We aim to use this efficient network to get to concise but accurate models of digital images thanks to: a) the use of multiple scales in the RBF kernel width, and b) the adoption of the forward regression orthogonal least squares (FROLS) algorithm to refine the model structure selection. Thereafter the new tailored model is excited to produce output signals aimed at be compressed by the discrete cosine transform (DCT), adopted in this work to compact signals’ energy into a few coefficients. To recognise images as MSRBF networks, a mathematical modelling was done by considering the first ones as multiple-input single-output systems. Based on the new methodology a novel computer aided diagnosis (CAD) system for cancer detection in X-ray mammograms was designed. Classification results show that the new CAD method helped reach a competitive diagnostic accuracy of 93.5%. It was similarly found that the MSRBF network is able to construct tailored and precise image models.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, including reprinting/ republishing this material for advertising or promotional purposes, creating new collective works for resale or redistribution to servers or lists, or reuse of any copyrighted components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Nonlinear system identification; Image processing; Discrete Cosine Transform; Radial Basis Functions; Computer Aided Diagnosis; Neural Networks |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Automatic Control and Systems Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 25 Jul 2018 13:50 |
Last Modified: | 01 Jul 2020 00:38 |
Status: | Published |
Publisher: | IEEE |
Refereed: | Yes |
Identification Number: | 10.23919/IConAC.2018.8748965 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:133452 |