MRI-based diagnosis of brain tumours using a deep neural network framework

Milan Acharya, Abeer Alsadoon, Shahd Al-Janabi, P. W.C. Prasad, Ahmed Dawoud, Ghossoon Alsadoon, Manoranjan Paul

    Research output: Chapter in Book/Report/Conference proceedingConference Paper published in Proceedingspeer-review

    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 languageEnglish
    Title of host publicationCITISIA 2020 - IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, Proceedings
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Pages83-87
    Number of pages5
    Edition1
    ISBN (Electronic)9781728194370
    ISBN (Print)9781728194387
    DOIs
    Publication statusPublished - 2020
    Event5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020 - Sydney, Australia
    Duration: 25 Nov 202027 Nov 2020

    Publication series

    NameCITISIA 2020 - IEEE Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, Proceedings

    Conference

    Conference5th IEEE International Conference on Innovative Technologies in Intelligent Systems and Industrial Applications, CITISIA 2020
    Country/TerritoryAustralia
    CitySydney
    Period25/11/2027/11/20

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