Brain Tumor Detection From MRI Images Using Deep Learning Techniques

Brain Tumor Detection From MRI Images Using Deep Learning Techniques

Anu Sharma
Copyright: © 2024 |Pages: 8
DOI: 10.4018/979-8-3693-2964-1.ch007
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Abstract

Machine learning and deep learning algorithms are utilized to identify brain tumors in a number of research papers. When these algorithms are applied to MRI images, it takes exceedingly slight time to expect a brain tumor, and the increased accuracy makes it easier to treat patients. The performance of the hybrid Convolution Neural Network (CNN) used in the proposed work to detect the existence of brain tumours is examined. In this study, we suggested a hybrid convolutional neural network followed by deep learning techniques using 2D magnetic resonance brain pictures, segment brain tumors (MRI). In our research, hybrid CNN achieved an accuracy of 98.73%, outperforming the results so far.
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Methodology

To further validate our work, we used SVM classifier and additional activation algorithms. The following steps are taken in order to apply CNN to the brain tumour dataset:

  • 1.

    Import the necessary packages

  • 2.

    Secondly, import the data folder (Yes/No)

  • 3.

    Assign photos a class label (1 for brain tumour, 0 for no brain tumour).

  • 4.

    Create 256x256-pixel shapes out of the photos.

  • 5.

    Make the Image Normal

  • 6.

    Separate the photos into the test, train, and validation sets.

  • 7.

    Build the chronological model.

  • 8.

    Put the model together.

  • 9.

    Use the train dataset as an example.

  • 10.

    Use the test photos to evaluate the model.

  • 11.

    Draw a graph comparing the accuracy during training and validation.

  • 12.

    Create a confusion matrix comparing actual and expected output.

Figure 1.

Workflow of the proposed CNN model

979-8-3693-2964-1.ch007.f01

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