Neurofuzzy Modelling as Part of an Intelligent Oncology Workstation in Breast Cancer Treatment
Neurofuzzy Modelling as Part of an Intelligent Oncology Workstation in Breast Cancer Treatment
In this paper an outline is presented of a neurofuzzy modelling approach as part of an Multimedia Intelligent Oncology Workstation (MINOW) for the improved treatment and diagnosis of breast cancer. The core component of the system is a high-dimensional approximator neurofuzzy network, ASMOD, introduced by Kavli and implemented by researchers at the University of Southampton. This neurofuzzy constructive learning algorithm may be used to automatically generate high-dimensional approximations to identify complex, possibly hidden, relationships between selected input variables and the measured output. The resulting neurofuzzy models may be interpreted as sets of linguistic fuzzy rules. This work is essentially concerned with fuzzy and neurofuzzy model building as part of a workstation aimed at improving and standardising treatment protocols in the diagnosis and treatment of breast cancer.
C110-C115
Bridgett, N.A.
25b96061-a19f-46cf-bef4-b63b56fb5fe1
Brandt, J.
fe36e0bd-893a-4b42-86f6-d0b6c183b8db
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
May 1995
Bridgett, N.A.
25b96061-a19f-46cf-bef4-b63b56fb5fe1
Brandt, J.
fe36e0bd-893a-4b42-86f6-d0b6c183b8db
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Bridgett, N.A., Brandt, J. and Harris, C.J.
(1995)
Neurofuzzy Modelling as Part of an Intelligent Oncology Workstation in Breast Cancer Treatment.
Int. Symp ICSC-ISFL'95.
.
Record type:
Conference or Workshop Item
(Other)
Abstract
In this paper an outline is presented of a neurofuzzy modelling approach as part of an Multimedia Intelligent Oncology Workstation (MINOW) for the improved treatment and diagnosis of breast cancer. The core component of the system is a high-dimensional approximator neurofuzzy network, ASMOD, introduced by Kavli and implemented by researchers at the University of Southampton. This neurofuzzy constructive learning algorithm may be used to automatically generate high-dimensional approximations to identify complex, possibly hidden, relationships between selected input variables and the measured output. The resulting neurofuzzy models may be interpreted as sets of linguistic fuzzy rules. This work is essentially concerned with fuzzy and neurofuzzy model building as part of a workstation aimed at improving and standardising treatment protocols in the diagnosis and treatment of breast cancer.
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Published date: May 1995
Additional Information:
Organisation: ICSC Address: Zurich, Switzerland
Venue - Dates:
Int. Symp ICSC-ISFL'95, 1995-05-01
Organisations:
Southampton Wireless Group
Identifiers
Local EPrints ID: 250239
URI: http://eprints.soton.ac.uk/id/eprint/250239
PURE UUID: e8ca0907-0b56-4ddc-bfe0-92f80edea50f
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Date deposited: 04 May 1999
Last modified: 22 Jul 2022 17:55
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Contributors
Author:
N.A. Bridgett
Author:
J. Brandt
Author:
C.J. Harris
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