A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

Rehman, A; Naz, S; Razzak, MI; Akram, F; Imran, M

A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning

Rehman, A Naz, S Razzak, MI Akram, F Imran, M

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Publisher:: Springer (part of Springer Nature)
Publication Type:: Journal Article
Citation:: Circuits, Systems and Signal Processing, 2020, 39, (2), pp. 757-775
Issue Date:: 2020

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Field	Value	Language
dc.contributor.author	Rehman, A
dc.contributor.author	Naz, S
dc.contributor.author	Razzak, MI
dc.contributor.author	Akram, F
dc.contributor.author	Imran, M
dc.date.accessioned	2020-10-15T10:30:31Z
dc.date.available	2020-10-15T10:30:31Z
dc.date.issued	2020
dc.identifier.citation	Circuits, Systems and Signal Processing, 2020, 39, (2), pp. 757-775
dc.identifier.issn	0278-081X
dc.identifier.issn	1531-5878
dc.identifier.uri	http://hdl.handle.net/10453/143277
dc.description.abstract	© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.
dc.language	en
dc.publisher	Springer (part of Springer Nature)
dc.relation.ispartof	Circuits, Systems and Signal Processing
dc.relation.isbasedon	10.1007/s00034-019-01246-3
dc.rights	info:eu-repo/semantics/restrictedAccess
dc.subject	0102 Applied Mathematics, 0906 Electrical and Electronic Engineering
dc.subject.classification	Industrial Engineering & Automation
dc.title	A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning
dc.type	Journal Article
utslib.citation.volume	39
utslib.for	0102 Applied Mathematics
utslib.for	0906 Electrical and Electronic Engineering
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Students
pubs.organisational-group	/University of Technology Sydney
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2020-10-15T10:30:19Z
pubs.issue	2
pubs.publication-status	Published
pubs.volume	39
utslib.citation.issue	2

Abstract:

© 2019, Springer Science+Business Media, LLC, part of Springer Nature. Brain tumors are the most destructive disease, leading to a very short life expectancy in their highest grade. The misdiagnosis of brain tumors will result in wrong medical intercession and reduce chance of survival of patients. The accurate diagnosis of brain tumor is a key point to make a proper treatment planning to cure and improve the existence of patients with brain tumors disease. The computer-aided tumor detection systems and convolutional neural networks provided success stories and have made important strides in the field of machine learning. The deep convolutional layers extract important and robust features automatically from the input space as compared to traditional predecessor neural network layers. In the proposed framework, we conduct three studies using three architectures of convolutional neural networks (AlexNet, GoogLeNet, and VGGNet) to classify brain tumors such as meningioma, glioma, and pituitary. Each study then explores the transfer learning techniques, i.e., fine-tune and freeze using MRI slices of brain tumor dataset—Figshare. The data augmentation techniques are applied to the MRI slices for generalization of results, increasing the dataset samples and reducing the chance of over-fitting. In the proposed studies, the fine-tune VGG16 architecture attained highest accuracy up to 98.69 in terms of classification and detection.

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http://hdl.handle.net/10453/143277