Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

Manan Binth Taj Noor; Nusrat Zerin Zenia; M. Shamim Kaiser; Mufti Mahmud; Shamim Al Mamun

doi:10.1007/978-3-030-37078-7_12

Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

Manan Binth Taj Noor
, Nusrat Zerin Zenia
, M. Shamim Kaiser^*
, Mufti Mahmud
, Shamim Al Mamun

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

89 Scopus citations

Abstract

Rapid development of high speed computing devices and infrastructure along with improved understanding of deep machine learning techniques during the last decade have opened up possibilities for advanced analysis of neuroimaging data. Using those computing tools Neuroscientists now can identify Neurodegenerative diseases from neuroimaging data. Due to the similarities in disease phenotypes, accurate detection of such disorders from neuroimaging data is very challenging. In this article, we have reviewed the methodological research papers proposing to detect neurodegenerative diseases using deep machine learning techniques only from MRI data. The results show that deep learning based techniques can detect the level of disorder with relatively high accuracy. Towards the end, current challenges are reviewed and some possible future research directions are provided.

Original language	English
Title of host publication	Brain Informatics - 12th International Conference, BI 2019, Proceedings
Editors	Peipeng Liang, Vinod Goel, Chunlei Shan
Publisher	Springer
Pages	115-125
Number of pages	11
ISBN (Print)	9783030370770
DOIs	https://doi.org/10.1007/978-3-030-37078-7_12
State	Published - 2019
Externally published	Yes
Event	12th International Conference on Brain Informatics, BI 2019 - Haikou, China Duration: 13 Dec 2019 → 15 Dec 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	11976 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th International Conference on Brain Informatics, BI 2019
Country/Territory	China
City	Haikou
Period	13/12/19 → 15/12/19

Bibliographical note

Publisher Copyright:
© 2019, Springer Nature Switzerland AG.

Keywords

Alzheimer’s disease
MRI
Machine learning
Parkinson’s disease
Schizophrenia

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-030-37078-7_12

Cite this

Noor, M. B. T., Zenia, N. Z., Kaiser, M. S., Mahmud, M., & Al Mamun, S. (2019). Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective. In P. Liang, V. Goel, & C. Shan (Eds.), Brain Informatics - 12th International Conference, BI 2019, Proceedings (pp. 115-125). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI). Springer. https://doi.org/10.1007/978-3-030-37078-7_12

Noor, Manan Binth Taj ; Zenia, Nusrat Zerin ; Kaiser, M. Shamim et al. / Detecting Neurodegenerative Disease from MRI : A Brief Review on a Deep Learning Perspective. Brain Informatics - 12th International Conference, BI 2019, Proceedings. editor / Peipeng Liang ; Vinod Goel ; Chunlei Shan. Springer, 2019. pp. 115-125 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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title = "Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective",

abstract = "Rapid development of high speed computing devices and infrastructure along with improved understanding of deep machine learning techniques during the last decade have opened up possibilities for advanced analysis of neuroimaging data. Using those computing tools Neuroscientists now can identify Neurodegenerative diseases from neuroimaging data. Due to the similarities in disease phenotypes, accurate detection of such disorders from neuroimaging data is very challenging. In this article, we have reviewed the methodological research papers proposing to detect neurodegenerative diseases using deep machine learning techniques only from MRI data. The results show that deep learning based techniques can detect the level of disorder with relatively high accuracy. Towards the end, current challenges are reviewed and some possible future research directions are provided.",

keywords = "Alzheimer{\textquoteright}s disease, MRI, Machine learning, Parkinson{\textquoteright}s disease, Schizophrenia",

author = "Noor, \{Manan Binth Taj\} and Zenia, \{Nusrat Zerin\} and Kaiser, \{M. Shamim\} and Mufti Mahmud and \{Al Mamun\}, Shamim",

note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 12th International Conference on Brain Informatics, BI 2019 ; Conference date: 13-12-2019 Through 15-12-2019",

year = "2019",

doi = "10.1007/978-3-030-37078-7\_12",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

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Noor, MBT, Zenia, NZ, Kaiser, MS, Mahmud, M & Al Mamun, S 2019, Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective. in P Liang, V Goel & C Shan (eds), Brain Informatics - 12th International Conference, BI 2019, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 11976 LNAI, Springer, pp. 115-125, 12th International Conference on Brain Informatics, BI 2019, Haikou, China, 13/12/19. https://doi.org/10.1007/978-3-030-37078-7_12

Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective. / Noor, Manan Binth Taj; Zenia, Nusrat Zerin; Kaiser, M. Shamim et al.
Brain Informatics - 12th International Conference, BI 2019, Proceedings. ed. / Peipeng Liang; Vinod Goel; Chunlei Shan. Springer, 2019. p. 115-125 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11976 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Detecting Neurodegenerative Disease from MRI

T2 - 12th International Conference on Brain Informatics, BI 2019

AU - Noor, Manan Binth Taj

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AU - Kaiser, M. Shamim

AU - Mahmud, Mufti

AU - Al Mamun, Shamim

PY - 2019

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N2 - Rapid development of high speed computing devices and infrastructure along with improved understanding of deep machine learning techniques during the last decade have opened up possibilities for advanced analysis of neuroimaging data. Using those computing tools Neuroscientists now can identify Neurodegenerative diseases from neuroimaging data. Due to the similarities in disease phenotypes, accurate detection of such disorders from neuroimaging data is very challenging. In this article, we have reviewed the methodological research papers proposing to detect neurodegenerative diseases using deep machine learning techniques only from MRI data. The results show that deep learning based techniques can detect the level of disorder with relatively high accuracy. Towards the end, current challenges are reviewed and some possible future research directions are provided.

AB - Rapid development of high speed computing devices and infrastructure along with improved understanding of deep machine learning techniques during the last decade have opened up possibilities for advanced analysis of neuroimaging data. Using those computing tools Neuroscientists now can identify Neurodegenerative diseases from neuroimaging data. Due to the similarities in disease phenotypes, accurate detection of such disorders from neuroimaging data is very challenging. In this article, we have reviewed the methodological research papers proposing to detect neurodegenerative diseases using deep machine learning techniques only from MRI data. The results show that deep learning based techniques can detect the level of disorder with relatively high accuracy. Towards the end, current challenges are reviewed and some possible future research directions are provided.

KW - Alzheimer’s disease

KW - MRI

KW - Machine learning

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KW - Schizophrenia

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T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 115

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BT - Brain Informatics - 12th International Conference, BI 2019, Proceedings

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ER -

Noor MBT, Zenia NZ, Kaiser MS, Mahmud M, Al Mamun S. Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective. In Liang P, Goel V, Shan C, editors, Brain Informatics - 12th International Conference, BI 2019, Proceedings. Springer. 2019. p. 115-125. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-37078-7_12

Detecting Neurodegenerative Disease from MRI: A Brief Review on a Deep Learning Perspective

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

Fingerprint

Cite this