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    Distributed computing methodology for training neural networks in an image-guided diagnostic application

    Plagianakos, V.P. and Magoulas, George D. and Vrahatis, M.N. (2006) Distributed computing methodology for training neural networks in an image-guided diagnostic application. Computer Methods and Programs in Biomedicine 81 (3), pp. 228-235. ISSN 0169-2607.

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    Abstract

    Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used.

    Metadata

    Item Type: Article
    Additional Information: Copyright © 2006 Elsevier Ireland Ltd.
    Keyword(s) / Subject(s): distributed computing, parallel implementations, parallel virtual machine—PVM, backpropagation training, image-guided diagnosis and surgery
    School: Birkbeck Schools and Departments > School of Business, Economics & Informatics > Computer Science and Information Systems
    Research Centre: Birkbeck Knowledge Lab
    Depositing User: Sandra Plummer
    Date Deposited: 11 Jun 2007
    Last Modified: 02 Dec 2016 13:23
    URI: http://eprints.bbk.ac.uk/id/eprint/503

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