Abstract
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.
Original language | English |
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Title of host publication | 2020 5th International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications (CITISIA) |
Place of Publication | United States |
Publisher | IEEE |
Pages | 1-5 |
Number of pages | 5 |
ISBN (Electronic) | 9781728194370 |
DOIs | |
Publication status | Published - 25 Nov 2020 |
Event | 5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020: CITISIA 2020 - Charles Sturt University Sydney campus, Sydney, Australia Duration: 25 Nov 2020 → 27 Nov 2020 https://web.archive.org/web/20201128085551/https://ieee-citisia.org/ (Conference website) https://web.archive.org/web/20210124015105/https://ieee-citisia.org/wp-content/uploads/2020/11/Conference-Program-new1.pdf (Conference program) https://ieeexplore.ieee.org/xpl/conhome/9371766/proceeding?pageNumber=4 (Full paper proceedings) |
Publication series
Name | CITISIA 2020 - IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, Proceedings |
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Conference
Conference | 5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020 |
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Country/Territory | Australia |
City | Sydney |
Period | 25/11/20 → 27/11/20 |
Other | The “Conference on Innovative Technologies in Intelligent Systems & Industrial Applications” (CITISIA) is a student conference that aims to provide students of higher learning institutions with a platform for presenting their own projects. It is also a measure of recognition of students’ professional and technical achievements – by industries and international organizations such as IEEE. This conference is designed to facilitate exchanges of ideas through communication, networking and learning from others, for students and IEEE Chapters in terms of greater collaboration. |
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