Residual Attention Network for Brain Tumour Classification
The main aim of this study is to design and produce an automated algorithm system using Residual Attention Network (RAN) model, which will classify brain tumour. In this project digitalised Magnetic Resonance Image (MRI) is used which is obtained from Malaysian hospitals. The MRI dataset consists of...
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Universiti Malaysia Sarawak (UNIMAS)
2019
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Online Access: | http://ir.unimas.my/id/eprint/27561/1/Residual%20attention%20network%20for%20brain%20tumor%20detection%20%2824%20pgs%29.pdf http://ir.unimas.my/id/eprint/27561/4/Sashwini%20ft.pdf http://ir.unimas.my/id/eprint/27561/ |
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my.unimas.ir.275612024-08-06T07:42:00Z http://ir.unimas.my/id/eprint/27561/ Residual Attention Network for Brain Tumour Classification Sashwini, A/P S. Thiagaraju H Social Sciences (General) RC0254 Neoplasms. Tumors. Oncology (including Cancer) The main aim of this study is to design and produce an automated algorithm system using Residual Attention Network (RAN) model, which will classify brain tumour. In this project digitalised Magnetic Resonance Image (MRI) is used which is obtained from Malaysian hospitals. The MRI dataset consists of those of patients who are 20 years and above both male and female. The Residual Attention Network model is trained and tested using the MRI dataset. The performance of the algorithm is evaluated based on training accuracy, testing accuracy, validate accuracy and validate loss and comparative analysis with Residual Neural Network (ResNet) and Convolutional Neural Network (CNN). ResNet and CNN were tested using the same dataset. Results from this study certainly proved that RAN provided the best performance among the 3 algorithms. ResNet has good performance with its accuracy ranging from 66% to 90%. The normal CNN algorithm did not perform well with the accuracy being very inconsistent between 57% and 71 %. RAN produced the highest and most consistent accuracy which is from 94% onwards. Further explanation is provided to prove the efficiency of Residual Attention Network for the classification of brain tumour. Universiti Malaysia Sarawak (UNIMAS) 2019 Final Year Project Report NonPeerReviewed text en http://ir.unimas.my/id/eprint/27561/1/Residual%20attention%20network%20for%20brain%20tumor%20detection%20%2824%20pgs%29.pdf text en http://ir.unimas.my/id/eprint/27561/4/Sashwini%20ft.pdf Sashwini, A/P S. Thiagaraju (2019) Residual Attention Network for Brain Tumour Classification. [Final Year Project Report] (Unpublished) |
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H Social Sciences (General) RC0254 Neoplasms. Tumors. Oncology (including Cancer) Sashwini, A/P S. Thiagaraju Residual Attention Network for Brain Tumour Classification |
description |
The main aim of this study is to design and produce an automated algorithm system using Residual Attention Network (RAN) model, which will classify brain tumour. In this project digitalised Magnetic Resonance Image (MRI) is used which is obtained from Malaysian hospitals. The MRI dataset consists of those of patients who are 20 years and above both male and female. The Residual Attention Network model is trained and tested using the MRI dataset. The performance of the algorithm is evaluated based on training accuracy, testing accuracy,
validate accuracy and validate loss and comparative analysis with Residual Neural Network (ResNet) and Convolutional Neural Network (CNN). ResNet and CNN were tested using the same dataset. Results from this study certainly proved that RAN provided the best performance
among the 3 algorithms. ResNet has good performance with its accuracy ranging from 66% to 90%. The normal CNN algorithm did not perform well with the accuracy being very inconsistent between 57% and 71 %. RAN produced the highest and most consistent accuracy which is from 94% onwards. Further explanation is provided to prove the efficiency of Residual Attention Network for the classification of brain tumour. |
format |
Final Year Project Report |
author |
Sashwini, A/P S. Thiagaraju |
author_facet |
Sashwini, A/P S. Thiagaraju |
author_sort |
Sashwini, A/P S. Thiagaraju |
title |
Residual Attention Network for Brain Tumour Classification |
title_short |
Residual Attention Network for Brain Tumour Classification |
title_full |
Residual Attention Network for Brain Tumour Classification |
title_fullStr |
Residual Attention Network for Brain Tumour Classification |
title_full_unstemmed |
Residual Attention Network for Brain Tumour Classification |
title_sort |
residual attention network for brain tumour classification |
publisher |
Universiti Malaysia Sarawak (UNIMAS) |
publishDate |
2019 |
url |
http://ir.unimas.my/id/eprint/27561/1/Residual%20attention%20network%20for%20brain%20tumor%20detection%20%2824%20pgs%29.pdf http://ir.unimas.my/id/eprint/27561/4/Sashwini%20ft.pdf http://ir.unimas.my/id/eprint/27561/ |
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1806693468667379712 |
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13.209306 |