TY - GEN
T1 - MRI-based diagnosis of brain tumours using a deep neural network framework
AU - Acharya, Milan
AU - Alsadoon, Abeer
AU - Al-Janabi, Shahd
AU - Prasad, P. W.C.
AU - Dawoud, Ahmed
AU - Alsadoon, Ghossoon
AU - Paul, Manoranjan
N1 - Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020
Y1 - 2020
N2 - The median survival time of patients with high grade glioma, a form of brain tumour, is 1-3 years. The current best practice adopts Convolutional Neural Network (CNN) for image classification and tumour detection. This method provides a significant improvement in brain tumour segmentation of Magnetic Resonance Imaging (MRI) images in comparison to other frameworks, but it is nonetheless slow and lacks precision. We sought to build upon the current best practice model by utilising a Deep Neural Network (DNN) model, which entailed modification of the segmentation and feature-extraction stages in order to improve the accuracy of those stages and the resulting segmentation. We contrasted the accuracy and efficiency of our model to the current best practice model using 10 brain tumour patient MRI datasets. First, the segmentation accuracy of our proposed model (M= 90%) outperformed that of the current best practice (M=78%). Second, the tumour detection processing time of our proposed model (M=34 ms) also outperformed that of the current best practice (M=73 ms). We, therefore, replicated previous studies by showing that automatic segmentation can aid in brain tumour detection. Importantly, we extended previous studies by proposing a model that classifies a brain tumour with greater accuracy and within lower processing times. Validation of the model with a larger dataset is recommended.
AB - The median survival time of patients with high grade glioma, a form of brain tumour, is 1-3 years. The current best practice adopts Convolutional Neural Network (CNN) for image classification and tumour detection. This method provides a significant improvement in brain tumour segmentation of Magnetic Resonance Imaging (MRI) images in comparison to other frameworks, but it is nonetheless slow and lacks precision. We sought to build upon the current best practice model by utilising a Deep Neural Network (DNN) model, which entailed modification of the segmentation and feature-extraction stages in order to improve the accuracy of those stages and the resulting segmentation. We contrasted the accuracy and efficiency of our model to the current best practice model using 10 brain tumour patient MRI datasets. First, the segmentation accuracy of our proposed model (M= 90%) outperformed that of the current best practice (M=78%). Second, the tumour detection processing time of our proposed model (M=34 ms) also outperformed that of the current best practice (M=73 ms). We, therefore, replicated previous studies by showing that automatic segmentation can aid in brain tumour detection. Importantly, we extended previous studies by proposing a model that classifies a brain tumour with greater accuracy and within lower processing times. Validation of the model with a larger dataset is recommended.
KW - Brain tumour
KW - Convolutional neural networks
KW - Deep neural network
KW - Magnetic resonance imaging
KW - Tumour segmentation
UR - http://www.scopus.com/inward/record.url?scp=85103024650&partnerID=8YFLogxK
UR - https://conferencealerts.com/show-event?id=227926
U2 - 10.1109/CITISIA50690.2020.9371831
DO - 10.1109/CITISIA50690.2020.9371831
M3 - Conference Paper published in Proceedings
AN - SCOPUS:85103024650
SN - 9781728194387
T3 - CITISIA 2020 - IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, Proceedings
SP - 83
EP - 87
BT - CITISIA 2020 - IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, Proceedings
PB - IEEE, Institute of Electrical and Electronics Engineers
T2 - 5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020
Y2 - 25 November 2020 through 27 November 2020
ER -