Brain Tumour Classification using Deep Learning with Residual Attention Network : A Comparative Study
— The main goal of this paper is to evaluate the performance of deep learning with Residual Attention Network (RAN) for brain tumour classification. Digitalised Magnetic Resonance Image (MRI) datasets obtained from Malaysian hospitals and other sources are utilised in this paper. The MRI datase...
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| Main Authors: | , |
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| Format: | Proceeding |
| Language: | English |
| Published: |
IEEE
2021
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| Subjects: | |
| Online Access: | http://ir.unimas.my/id/eprint/35722/1/tumour1.pdf http://ir.unimas.my/id/eprint/35722/ https://ieeexplore.ieee.org/document/9493544 https://doi.org/10.1109/ICOTEN52080.2021.9493544 |
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| Summary: | — The main goal of this paper is to evaluate the
performance of deep learning with Residual Attention
Network (RAN) for brain tumour classification. Digitalised
Magnetic Resonance Image (MRI) datasets obtained from
Malaysian hospitals and other sources are utilised in this
paper. The MRI datasets consist of information of those
patients who are 20 years old and above, both male and
female. The RAN algorithm is trained and tested using the
MRI datasets. The algorithm performance is evaluated based
on training accuracy, testing accuracy, validation accuracy,
and validation loss metrices. Moreover, a comparative
analysis is done with Residual Neural Network (ResNet) and
Convolutional Neural Network (CNN) using the same
datasets. The findings from this study prove that RAN
provides the best performance among the three algorithms.
ResNet has good performance, with an accuracy ranging from
67% to 87%. The standard CNN algorithm does not perform
well, with a very inconsistent accuracy of between 57% and
71%. RAN produces the highest and most consistent
accuracy, which is 94% and above. Further explanation is
provided in this paper to prove the efficiency of RAN for the
classification of brain tumours |
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