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ANN Classification of MS Subgroups with Diffusion Limited Aggregation

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Computational Science and Its Applications – ICCSA 2018 (ICCSA 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10961))

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Abstract

In Diffusion Limited Aggregation (DLA), the procedure in which substances blend irrevocably to produce dendrites, is idealised. During this process, the slowest phase tends to be the diffusion of substance to aggregate. This study focuses on the procedure where substances enduring a random walk because of Brownian motion cluster together to form aggregates of such particles. Magnetic Resonance Image (MRI) is one of the methods used for identifying nervous system chronic disorders. MS_ dataset, comprised of MR images belonging to patients with one of the MS subgroups, was used in this study. The study aims at identifying the homogenous and self-similar pixels that the regions with lesions are located by applying the DLA onto the patients’ MR images in line with the following steps: (i) By applying the Diffusion Limited Aggregation (DLA) algorithm onto the MS_dataset (patients’ MRI) the regions with the lesion have been identified. Thus, DLA_MS dataset has been generated. (ii) Feed Forward Back Propagation (FFBP) and Cascade Forward Back Propagation (CFBP) algorithms, two of the artificial neural network algorithms, have been applied to the MS_dataset and DLA_MS dataset. MS subgroups have been classified accordingly. (iii) Classification Accuracy rates as obtained from the application of FFBP and CFBP algorithms on the MS_dataset and DLA_MS dataset have been compared. Having been done for the first time, it has been revealed, through the application of ANN algorithms, how the most significant pixels are identified within the relevant dataset through DLA.

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Author information

Authors and Affiliations

  1. University of Massachusetts Medical School, Worcester, MA, 01655, USA

    Yeliz Karaca

  2. Engineering School, DEIM, University of Tuscia, 01100, Viterbo, VT, Italy

    Carlo Cattani

  3. Department of Neurology, Hacettepe University, 06100, Ankara, Turkey

    Rana Karabudak

Authors
  1. Yeliz Karaca
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  2. Carlo Cattani
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  3. Rana Karabudak
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Corresponding author

Correspondence to Yeliz Karaca .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Italy

    Beniamino Murgante

  3. Covenant University, Ota, Nigeria

    Sanjay Misra

  4. Saint Petersburg State University, Saint Petersburg, Russia

    Elena Stankova

  5. Polytechnic University of Bari, Bari, Italy

    Carmelo M. Torre

  6. University of Minho, Braga, Portugal

    Ana Maria A.C. Rocha

  7. Monash University, Clayton, Victoria, Australia

    David Taniar

  8. Kyushu Sangyo University, Fukuoka shi, Fukuoka, Japan

    Bernady O. Apduhan

  9. Politecnico di Bari, Bari, Italy

    Eufemia Tarantino

  10. Myongji University, Yongin, Korea (Republic of)

    Yeonseung Ryu

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Karaca, Y., Cattani, C., Karabudak, R. (2018). ANN Classification of MS Subgroups with Diffusion Limited Aggregation. In: Gervasi, O., et al. Computational Science and Its Applications – ICCSA 2018. ICCSA 2018. Lecture Notes in Computer Science(), vol 10961. Springer, Cham. https://doi.org/10.1007/978-3-319-95165-2_9

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  • DOI: https://doi.org/10.1007/978-3-319-95165-2_9

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-95164-5

  • Online ISBN: 978-3-319-95165-2

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