Automated detection of schizophrenia from brain MRI slices using optimized deep-features

Seifedine Kadry; David Taniar; Robertas Damasevicius; Venkatesan Rajinikanth

doi:10.1109/ICBSII51839.2021.9445133

Automated detection of schizophrenia from brain MRI slices using optimized deep-features

Seifedine Kadry, David Taniar, Robertas Damasevicius, Venkatesan Rajinikanth

Department of Software Systems & Cybersecurity

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

15 Citations (Scopus)

Abstract

In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system.

Original language	English
Title of host publication	Proceedings of 2021 IEEE Seventh International Conference on Bio Signals, Images and Instrumentation
Place of Publication	Piscataway NJ USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	303-307
Number of pages	5
ISBN (Electronic)	9781665441261
ISBN (Print)	9781665431002
DOIs	https://doi.org/10.1109/ICBSII51839.2021.9445133
Publication status	Published - 2021
Event	IEEE International Conference on Bio Signals, Images and Instrumentation 2021 - Chennai, India Duration: 25 Mar 2021 → 27 Mar 2021 Conference number: 7th https://ieeexplore.ieee.org/xpl/conhome/9445117/proceeding (Proceedings)

Conference

Conference	IEEE International Conference on Bio Signals, Images and Instrumentation 2021
Abbreviated title	ICBSII 2021
Country/Territory	India
City	Chennai
Period	25/03/21 → 27/03/21
Internet address	https://ieeexplore.ieee.org/xpl/conhome/9445117/proceeding (Proceedings)

Keywords

Brain MRI
Mental illness
Schizophrenia
Slime-Mould-Algorithm
SVM-Cubic
VGG16

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/ICBSII51839.2021.9445133

Cite this

Kadry, S., Taniar, D., Damasevicius, R., & Rajinikanth, V. (2021). Automated detection of schizophrenia from brain MRI slices using optimized deep-features. In Proceedings of 2021 IEEE Seventh International Conference on Bio Signals, Images and Instrumentation (pp. 303-307). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/ICBSII51839.2021.9445133

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abstract = "In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system. ",

keywords = "Brain MRI, Mental illness, Schizophrenia, Slime-Mould-Algorithm, SVM-Cubic, VGG16",

author = "Seifedine Kadry and David Taniar and Robertas Damasevicius and Venkatesan Rajinikanth",

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Kadry, S, Taniar, D, Damasevicius, R & Rajinikanth, V 2021, Automated detection of schizophrenia from brain MRI slices using optimized deep-features. in Proceedings of 2021 IEEE Seventh International Conference on Bio Signals, Images and Instrumentation. IEEE, Institute of Electrical and Electronics Engineers, Piscataway NJ USA, pp. 303-307, IEEE International Conference on Bio Signals, Images and Instrumentation 2021, Chennai, India, 25/03/21. https://doi.org/10.1109/ICBSII51839.2021.9445133

Automated detection of schizophrenia from brain MRI slices using optimized deep-features. / Kadry, Seifedine; Taniar, David; Damasevicius, Robertas et al.
Proceedings of 2021 IEEE Seventh International Conference on Bio Signals, Images and Instrumentation. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers, 2021. p. 303-307.

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

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AU - Kadry, Seifedine

AU - Taniar, David

AU - Damasevicius, Robertas

AU - Rajinikanth, Venkatesan

N1 - Conference code: 7th

PY - 2021

Y1 - 2021

N2 - In humans, the incident rate of mental illness is gradually rising due to various causes. Schizophrenia is one of the chronic mental illness and its happing rate also rising in the current era. The patient with Schizophrenia will experience a confused mental condition and a timely recognition and treatment is essential to reduce the risk. The proposed work aims to implement a methodology to support the automated detection of Schizophrenia from the brain MRI slices of T1 modality (T1W). The assessment of brain MRI is executed using the pre-trained VGG16 system and the deep-features extracted are optimized with the Slime-Mould-Algorithm (SMA) and the reduced features are then considered to train, test and validate the binary classifiers employed in this work. This research is implemented using 500 images of each case (healthy/abnormal) and the attained result with the SVM-Cubic is superior compared to other classifiers considered in the automated disease detection system.

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KW - Mental illness

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Kadry S, Taniar D, Damasevicius R, Rajinikanth V. Automated detection of schizophrenia from brain MRI slices using optimized deep-features. In Proceedings of 2021 IEEE Seventh International Conference on Bio Signals, Images and Instrumentation. Piscataway NJ USA: IEEE, Institute of Electrical and Electronics Engineers. 2021. p. 303-307 doi: 10.1109/ICBSII51839.2021.9445133

Automated detection of schizophrenia from brain MRI slices using optimized deep-features

Abstract

Conference

Keywords

UN SDGs

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