Structural MRI classification for Alzheimer's disease detection using deep belief network

Ratna Mufidah, Ito Wasito, Nurul Hanifah, Moh Faturrahman

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

7 Citations (Scopus)

Abstract

Early detection of Alzheimer's disease (AD) is the key of preventing, slowing, and stopping the disease. An early detection of AD can be performed by analyzing the neuro-imaging data. The magnetic resonance image (MRI) can be used as a modality of neuro-imaging data in order to detect AD. The MRI also have several advantages such as high-quality of spatial resolution, widely availability, adequate contrast and without requiring radioactive pharmaceutical injection during acquisition process. However, the main challenge of structural MRI data classification is the high dimensionality of the data. Therefore, this study proposes a classification method of AD based on structural modalities using Deep Belief Network (DBN) which is has power in term of predictive models. Support vector machine (SVM) has been used as a comparative classification model againts DBN. The result shows that this approach outperforms SVM and current method in previous study. The DBN achieves 0.9176, 0.9059 and 0.9296 in accuracy, sensitivity and specificity.

Original languageEnglish
Title of host publicationProceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages37-42
Number of pages6
ISBN (Electronic)9781538628256
DOIs
Publication statusPublished - 19 Jan 2018
Event11th International Conference on Information and Communication Technology and System, ICTS 2017 - Surabaya, Indonesia
Duration: 31 Oct 201731 Oct 2017

Publication series

NameProceedings of the 11th International Conference on Information and Communication Technology and System, ICTS 2017
Volume2018-January

Conference

Conference11th International Conference on Information and Communication Technology and System, ICTS 2017
CountryIndonesia
CitySurabaya
Period31/10/1731/10/17

Keywords

  • Alzheimer's disease
  • Deep Belief Network
  • high-dimensional data
  • structural MRI

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