A Hybrid Deep Model for Brain Tumor Classification

Hamail Ayaz; Muhammad Ahmad; David Tormey; Ian McLoughlin; Saritha Unnikrishnan

doi:10.1007/978-981-16-3880-0_29

A Hybrid Deep Model for Brain Tumor Classification

Hamail Ayaz^*, Muhammad Ahmad, David Tormey, Ian McLoughlin, Saritha Unnikrishnan

^*Corresponding author for this work

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

3 Scopus citations

Abstract

Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods.

Original language	English
Title of host publication	Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis
Editors	Ruidan Su, Yu-Dong Zhang, Han Liu
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	282-291
Number of pages	10
ISBN (Print)	9789811638794
DOIs	https://doi.org/10.1007/978-981-16-3880-0_29
State	Published - 2022
Externally published	Yes
Event	International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Virtual, Online Duration: 25 Mar 2021 → 26 Mar 2021

Publication series

Name	Lecture Notes in Electrical Engineering
Volume	784 LNEE
ISSN (Print)	1876-1100
ISSN (Electronic)	1876-1119

Conference

Conference	International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021
City	Virtual, Online
Period	25/03/21 → 26/03/21

Bibliographical note

Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

Keywords

Brain tumor
Classification
Ensemble learning
Hybrid model

ASJC Scopus subject areas

Industrial and Manufacturing Engineering

UN SDGs

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

Access to Document

10.1007/978-981-16-3880-0_29

Cite this

Ayaz, H., Ahmad, M., Tormey, D., McLoughlin, I., & Unnikrishnan, S. (2022). A Hybrid Deep Model for Brain Tumor Classification. In R. Su, Y.-D. Zhang, & H. Liu (Eds.), Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis (pp. 282-291). (Lecture Notes in Electrical Engineering; Vol. 784 LNEE). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-981-16-3880-0_29

Ayaz, Hamail ; Ahmad, Muhammad ; Tormey, David et al. / A Hybrid Deep Model for Brain Tumor Classification. Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. editor / Ruidan Su ; Yu-Dong Zhang ; Han Liu. Springer Science and Business Media Deutschland GmbH, 2022. pp. 282-291 (Lecture Notes in Electrical Engineering).

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title = "A Hybrid Deep Model for Brain Tumor Classification",

abstract = "Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI{\textquoteright}s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94\%), Precision (92\%), Recall (95\%), and F1 score (93\%) and outperformed many state of the art hybrid methods.",

keywords = "Brain tumor, Classification, Ensemble learning, Hybrid model",

author = "Hamail Ayaz and Muhammad Ahmad and David Tormey and Ian McLoughlin and Saritha Unnikrishnan",

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year = "2022",

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Ayaz, H, Ahmad, M, Tormey, D, McLoughlin, I & Unnikrishnan, S 2022, A Hybrid Deep Model for Brain Tumor Classification. in R Su, Y-D Zhang & H Liu (eds), Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. Lecture Notes in Electrical Engineering, vol. 784 LNEE, Springer Science and Business Media Deutschland GmbH, pp. 282-291, International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021, Virtual, Online, 25/03/21. https://doi.org/10.1007/978-981-16-3880-0_29

A Hybrid Deep Model for Brain Tumor Classification. / Ayaz, Hamail; Ahmad, Muhammad; Tormey, David et al.
Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. ed. / Ruidan Su; Yu-Dong Zhang; Han Liu. Springer Science and Business Media Deutschland GmbH, 2022. p. 282-291 (Lecture Notes in Electrical Engineering; Vol. 784 LNEE).

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

TY - GEN

T1 - A Hybrid Deep Model for Brain Tumor Classification

AU - Ayaz, Hamail

AU - Ahmad, Muhammad

AU - Tormey, David

AU - McLoughlin, Ian

AU - Unnikrishnan, Saritha

PY - 2022

Y1 - 2022

N2 - Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods.

AB - Classification of brain tumors from Magnetic Resonance Images (MRIs) using Computer-Aided Diagnosis (CAD) has faced some major challenges. Diagnosis of brain tumors such as glioma, meningioma, and pituitary mostly rely on manual evaluation by neuro-radiologists and is prone to human error and subjectivity. In recent years, Machine Learning (ML) techniques have been used to improve the accuracy of tumor diagnosis with the expense of intensive pre-processing and computational cost. Therefore, this work proposed a hybrid Convolutional Neural Network (CNN) (i.e., AlexNet followed by SqueezeNet) to extract quality tumor biomarkers for better performance of the CAD system using brain tumor MRI’s. The features extracted using AlexNet and SqueezeNet are fused to preserve the most important biomarkers in a computationally efficient manner. A total of 3064 brain tumors (708 Meningioma, 1426 Glioma, and 930 Pituitaries) MRIs have been experimented. The proposed model is evaluated using several well-known metrics, i.e., Overall accuracy (94%), Precision (92%), Recall (95%), and F1 score (93%) and outperformed many state of the art hybrid methods.

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KW - Classification

KW - Ensemble learning

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DO - 10.1007/978-981-16-3880-0_29

M3 - Conference contribution

AN - SCOPUS:85115127327

SN - 9789811638794

T3 - Lecture Notes in Electrical Engineering

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EP - 291

BT - Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis

A2 - Su, Ruidan

A2 - Zhang, Yu-Dong

A2 - Liu, Han

PB - Springer Science and Business Media Deutschland GmbH

T2 - International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021

Y2 - 25 March 2021 through 26 March 2021

ER -

Ayaz H, Ahmad M, Tormey D, McLoughlin I, Unnikrishnan S. A Hybrid Deep Model for Brain Tumor Classification. In Su R, Zhang YD, Liu H, editors, Proceedings of 2021 International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021 - Medical Imaging and Computer-Aided Diagnosis. Springer Science and Business Media Deutschland GmbH. 2022. p. 282-291. (Lecture Notes in Electrical Engineering). doi: 10.1007/978-981-16-3880-0_29

A Hybrid Deep Model for Brain Tumor Classification

Abstract

Publication series

Conference

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

Access to Document

Fingerprint

Cite this