Automated detection of schizophrenia using deep learning: a review for the last decade

Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for fe...

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Bibliographic Details
Published in:Physiological measurement 2023-03, Vol.44 (3), p.3
Main Authors: Sharma, Manish, Patel, Ruchit Kumar, Garg, Akshat, SanTan, Ru, Acharya, U Rajendra
Format: Article
Language:English
Subjects:
convolutional neural networks
Deep Learning
electroencephalography (EEG)
Electroencephalography - methods
functional magnetic resonance imaging
Humans
long short-term memory
Machine Learning
Neural Networks, Computer
schizophrenia
Schizophrenia - diagnostic imaging
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Summary:Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.
ISSN:0967-3334
1361-6579
DOI:10.1088/1361-6579/acb24d