Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study

Seifedine Kadry; Yunyoung Nam; Hafiz Tayyab Rauf; Venkatesan Rajinikanth; Isah A. Lawal

doi:10.1109/ICBSII51839.2021.9445122

Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study

Seifedine Kadry
, Yunyoung Nam
, Hafiz Tayyab Rauf
, Venkatesan Rajinikanth
, Isah A. Lawal

Noroff University College
Soonchunhyang University
University of Staffordshire
Anna University

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

37 Scopus citations

Abstract

Brain is the vital organ in human physiology; which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes; normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed.

Original language	English
Title of host publication	Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781665441261
DOIs	https://doi.org/10.1109/ICBSII51839.2021.9445122
State	Published - 25 Mar 2021
Externally published	Yes
Event	7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021 - Chennai, India Duration: 25 Mar 2021 → 27 Mar 2021

Publication series

Name	Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

Conference

Conference	7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021
Country/Territory	India
City	Chennai
Period	25/03/21 → 27/03/21

UN SDGs

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

SDG 3 Good Health and Well-being

Keywords

Accuracy
Brain abnormality
DeepLearning
MRI slices
Multi-Class classification

Access to Document

10.1109/ICBSII51839.2021.9445122

Cite this

Kadry, S., Nam, Y., Rauf, H. T., Rajinikanth, V., & Lawal, I. A. (2021). Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study. In Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021 Article 9445122 (Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICBSII51839.2021.9445122

Kadry, Seifedine ; Nam, Yunyoung ; Rauf, Hafiz Tayyab et al. / Automated Detection of Brain Abnormality using Deep-Learning-Scheme : A Study. Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021).

@inproceedings{a5451c7b870b42358f11da1c913dbcd4,

title = "Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study",

abstract = "Brain is the vital organ in human physiology; which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes; normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed.",

keywords = "Accuracy, Brain abnormality, DeepLearning, MRI slices, Multi-Class classification",

author = "Seifedine Kadry and Yunyoung Nam and Rauf, \{Hafiz Tayyab\} and Venkatesan Rajinikanth and Lawal, \{Isah A.\}",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021 ; Conference date: 25-03-2021 Through 27-03-2021",

year = "2021",

month = mar,

day = "25",

doi = "10.1109/ICBSII51839.2021.9445122",

language = "English",

series = "Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021",

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address = "United States",

}

Kadry, S, Nam, Y, Rauf, HT, Rajinikanth, V & Lawal, IA 2021, Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study. in Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021., 9445122, Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021, Institute of Electrical and Electronics Engineers Inc., 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021, Chennai, India, 25/03/21. https://doi.org/10.1109/ICBSII51839.2021.9445122

Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study. / Kadry, Seifedine; Nam, Yunyoung; Rauf, Hafiz Tayyab et al.
Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021. Institute of Electrical and Electronics Engineers Inc., 2021. 9445122 (Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021).

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

TY - GEN

T1 - Automated Detection of Brain Abnormality using Deep-Learning-Scheme

T2 - 7th IEEE International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

AU - Kadry, Seifedine

AU - Nam, Yunyoung

AU - Rauf, Hafiz Tayyab

AU - Rajinikanth, Venkatesan

AU - Lawal, Isah A.

PY - 2021/3/25

Y1 - 2021/3/25

N2 - Brain is the vital organ in human physiology; which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes; normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed.

AB - Brain is the vital organ in human physiology; which is conscientious for sensory signal handling and judgment making. The irregularity in brain severely influence entire decision making procedure and the unrecognized and untreated defect will lead to various harsh conditions. This research aims to implement pre-trained Deep-Learning-Scheme (DLS) to classify the brain MRI slices using a multi-class classifier. In this research, the brain MRI slices with classes; normal, stroke, Low-Grade-Glioma (LGG) and High-Grade-Glioma (HGG) are considered for the experimental study. In this work every test picture is resized into 224x224x3 pixels and these imagery are then considered to validate the performance of DLS, such as VGG16, VGG19 and ResNet50 using different classifiers. The attained classification accuracy of every DLS with classifiers, SoftMax, SVM-RBF and SVM-Cubic are presented and discussed.

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KW - Brain abnormality

KW - DeepLearning

KW - MRI slices

KW - Multi-Class classification

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M3 - Conference contribution

AN - SCOPUS:85108003238

T3 - Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

BT - Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021

PB - Institute of Electrical and Electronics Engineers Inc.

Y2 - 25 March 2021 through 27 March 2021

ER -

Kadry S, Nam Y, Rauf HT, Rajinikanth V, Lawal IA. Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study. In Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021. Institute of Electrical and Electronics Engineers Inc. 2021. 9445122. (Proceedings of 2021 IEEE 7th International Conference on Bio Signals, Images and Instrumentation, ICBSII 2021). doi: 10.1109/ICBSII51839.2021.9445122

Automated Detection of Brain Abnormality using Deep-Learning-Scheme: A Study

Abstract

Publication series

Conference

UN SDGs

Keywords

Access to Document

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