Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements

Noushath Shaffi; Mufti Mahmud; Faizal Hajamohideen; Karthikeyan Subramanian; M. Shamim Kaiser

doi:10.1007/978-981-19-9638-2_72

Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements

Noushath Shaffi
, Mufti Mahmud^*
, Faizal Hajamohideen
, Karthikeyan Subramanian
, M. Shamim Kaiser

^*Corresponding author for this work

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

3 Scopus citations

Abstract

The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate analytics of medical data. Although it is a challenging task, the application of powerful AI-based computer-aided diagnosis has enabled health practitioners to predict Neurodegenerative diseases more accurately than ever before. This paper presents a brief review of methodological research papers dealing with the prediction of Schizophrenia disease based on machine learning and deep learning techniques using MRI and EEG signals data. The result shows that these techniques have permeated the Schizophrenia disease prediction/classification task with significantly higher accuracy. The paper concludes by providing future avenues based on challenges that are currently faced by this sector.

Original language	English
Title of host publication	Information and Communication Technology for Competitive Strategies, ICTCS 2022 - ICT
Subtitle of host publication	Applications and Social Interfaces
Editors	Amit Joshi, Mufti Mahmud, Roshan G. Ragel
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	849-866
Number of pages	18
ISBN (Print)	9789811996375
DOIs	https://doi.org/10.1007/978-981-19-9638-2_72
State	Published - 2023
Externally published	Yes
Event	7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022 - Chandigarh, India Duration: 9 Dec 2022 → 10 Dec 2022

Publication series

Name	Lecture Notes in Networks and Systems
Volume	623 LNNS
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

Conference

Conference	7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022
Country/Territory	India
City	Chandigarh
Period	9/12/22 → 10/12/22

Bibliographical note

Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Artificial intelligence
Deep learning
EEG
MRI
Machine learning
Schizophrenia

ASJC Scopus subject areas

Control and Systems Engineering
Signal Processing
Computer Networks and Communications

Access to Document

10.1007/978-981-19-9638-2_72

Cite this

Shaffi, N., Mahmud, M., Hajamohideen, F., Subramanian, K., & Shamim Kaiser, M. (2023). Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements. In A. Joshi, M. Mahmud, & R. G. Ragel (Eds.), Information and Communication Technology for Competitive Strategies, ICTCS 2022 - ICT: Applications and Social Interfaces (pp. 849-866). (Lecture Notes in Networks and Systems; Vol. 623 LNNS). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-19-9638-2_72

Shaffi, Noushath ; Mahmud, Mufti ; Hajamohideen, Faizal et al. / Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals : An Overview of the Recent Advancements. Information and Communication Technology for Competitive Strategies, ICTCS 2022 - ICT: Applications and Social Interfaces. editor / Amit Joshi ; Mufti Mahmud ; Roshan G. Ragel. Springer Science and Business Media Deutschland GmbH, 2023. pp. 849-866 (Lecture Notes in Networks and Systems).

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title = "Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements",

abstract = "The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate analytics of medical data. Although it is a challenging task, the application of powerful AI-based computer-aided diagnosis has enabled health practitioners to predict Neurodegenerative diseases more accurately than ever before. This paper presents a brief review of methodological research papers dealing with the prediction of Schizophrenia disease based on machine learning and deep learning techniques using MRI and EEG signals data. The result shows that these techniques have permeated the Schizophrenia disease prediction/classification task with significantly higher accuracy. The paper concludes by providing future avenues based on challenges that are currently faced by this sector.",

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author = "Noushath Shaffi and Mufti Mahmud and Faizal Hajamohideen and Karthikeyan Subramanian and \{Shamim Kaiser\}, M.",

note = "Publisher Copyright: {\textcopyright} 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.; 7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022 ; Conference date: 09-12-2022 Through 10-12-2022",

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doi = "10.1007/978-981-19-9638-2\_72",

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Shaffi, N, Mahmud, M, Hajamohideen, F, Subramanian, K & Shamim Kaiser, M 2023, Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements. in A Joshi, M Mahmud & RG Ragel (eds), Information and Communication Technology for Competitive Strategies, ICTCS 2022 - ICT: Applications and Social Interfaces. Lecture Notes in Networks and Systems, vol. 623 LNNS, Springer Science and Business Media Deutschland GmbH, pp. 849-866, 7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022, Chandigarh, India, 9/12/22. https://doi.org/10.1007/978-981-19-9638-2_72

Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements. / Shaffi, Noushath; Mahmud, Mufti; Hajamohideen, Faizal et al.
Information and Communication Technology for Competitive Strategies, ICTCS 2022 - ICT: Applications and Social Interfaces. ed. / Amit Joshi; Mufti Mahmud; Roshan G. Ragel. Springer Science and Business Media Deutschland GmbH, 2023. p. 849-866 (Lecture Notes in Networks and Systems; Vol. 623 LNNS).

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

TY - GEN

T1 - Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals

T2 - 7th International Conference on Information and Communication Technology for Competitive Strategies , ICTCS 2022

AU - Shaffi, Noushath

AU - Mahmud, Mufti

AU - Hajamohideen, Faizal

AU - Subramanian, Karthikeyan

AU - Shamim Kaiser, M.

PY - 2023

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AB - The availability of large-scale datasets, massively parallelizable GPUs and a wide spectrum of open-source tools have immensely capacitated Artificial Intelligence (AI) field in accurate analytics of medical data. Although it is a challenging task, the application of powerful AI-based computer-aided diagnosis has enabled health practitioners to predict Neurodegenerative diseases more accurately than ever before. This paper presents a brief review of methodological research papers dealing with the prediction of Schizophrenia disease based on machine learning and deep learning techniques using MRI and EEG signals data. The result shows that these techniques have permeated the Schizophrenia disease prediction/classification task with significantly higher accuracy. The paper concludes by providing future avenues based on challenges that are currently faced by this sector.

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ER -

Shaffi N, Mahmud M, Hajamohideen F, Subramanian K, Shamim Kaiser M. Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements. In Joshi A, Mahmud M, Ragel RG, editors, Information and Communication Technology for Competitive Strategies, ICTCS 2022 - ICT: Applications and Social Interfaces. Springer Science and Business Media Deutschland GmbH. 2023. p. 849-866. (Lecture Notes in Networks and Systems). doi: 10.1007/978-981-19-9638-2_72

Machine Learning and Deep Learning Methods for the Detection of Schizophrenia Using Magnetic Resonance Images and EEG Signals: An Overview of the Recent Advancements

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

Access to Document

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Cite this