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Segmentation of Multiple Sclerosis Using Autoencoder and Classifier

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Evolutionary Artificial Intelligence (ICEASSM 2017)

Part of the book series: Algorithms for Intelligent Systems ((AIS))

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Abstract

Multiple Sclerosis (MS) is an inflammatory demyelination of the central nervous system that progresses to complete or incomplete recovery of neurological dysfunction. Accurately identifying the degree of damage to the brain matter is critical for prognosis. Progression of MS must be closely monitored requiring regular assessment of symptoms through diagnostic methods like magnetic resonance imaging (MRI). This process is quite taxing and time-consuming to be done manually. This paper presents a methodology to perform automatic segmentation of multiple sclerosis via a combination approach of an unsupervised autoencoder and a pre-trained classifier for lesion detection followed by segmentation. The presented approach investigates the usability of an autoencoder for classification and segmentation. The novelty lies in reducing the reliance of the model on the availability of labeled data. This is a prevalent issue in the field of research for MS. The proposed approach has been evaluated on the FLAIR MRI images obtained from the members of Ozal University Medical Faculty in 2021 and is able to deliver good results.

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Authors and Affiliations

  1. Centre for Healthcare Advancement, Innovation and Research, Vellore Institute of Technology, Chennai, India

    Vijayarajan Rajangam

  2. School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India

    Sangeetha Nagarajan

  3. School of Electronics Engineering, Vellore Institute of Technology, Chennai, India

    M. Misba Farheen, Ananya Yayavaram & V. P. Nasheeda

Authors
  1. Vijayarajan Rajangam
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  2. Sangeetha Nagarajan
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  3. M. Misba Farheen
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  4. Ananya Yayavaram
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  5. V. P. Nasheeda
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Corresponding author

Correspondence to Vijayarajan Rajangam .

Editor information

Editors and Affiliations

  1. Faculty of Innovation and Technology, Taylor’s University, Subang Jaya, Selangor, Malaysia

    David Asirvatham

  2. University of Southeast Norway, Notodden, Norway

    Francisco M. Gonzalez-Longatt

  3. Gdansk University of Technology, Gdańsk, Poland

    Przemyslaw Falkowski-Gilski

  4. Professor of Computer Engineering, Papua New Guinea University of Technology, Lae, Papua New Guinea

    R. Kanthavel

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© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Rajangam, V., Nagarajan, S., Farheen, M.M., Yayavaram, A., Nasheeda, V.P. (2024). Segmentation of Multiple Sclerosis Using Autoencoder and Classifier. In: Asirvatham, D., Gonzalez-Longatt, F.M., Falkowski-Gilski, P., Kanthavel, R. (eds) Evolutionary Artificial Intelligence. ICEASSM 2017. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-99-8438-1_9

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