A comparison study of deep CNN models for brain tumor MRI image classification
Throughout the life journey, brain health conditions may emerge and are identified by disruptions in normal brain growth and brain functioning. One of the disruption that may manifest as a neurological condition is brain tumor, which incidence is increasing in recent years. Since manual method of...
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Format: | Thesis |
Language: | English English English |
Published: |
2021
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Online Access: | http://eprints.uthm.edu.my/6999/1/24p%20MOHD%20RIDZUAN%20MUHAMMAD%20IMRAN.pdf http://eprints.uthm.edu.my/6999/2/MOHD%20RIDZUAN%20MUHAMMAD%20IMRAN%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/6999/3/MOHD%20RIDZUAN%20MUHAMMAD%20IMRAN%20WATERMARK.pdf |
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Summary: | Throughout the life journey, brain health conditions may emerge and are identified by
disruptions in normal brain growth and brain functioning. One of the disruption that
may manifest as a neurological condition is brain tumor, which incidence is increasing
in recent years. Since manual method of classifying brain tumor images is tedious and
can only be done at certain diagnostic centers, an alternative way of using deep
learning technique to detect abnormal tissues in the brain is undertaken in this work.
In this project, different architectures of Convolutional Neural Network (CNN) models
namely AlexNet, Residual Network-18 (ResNet-18) and GoogLeNet were adopted
and compared for their performance in classification of brain tumor Magnetic
Resonance Imaging (MRI) images via two different approaches namely transfer
learning method and modified method. The performance of the CNN models were
evaluated and the most reliable model for the classification of brain tumor MRI images
was determined. From the comparison done between the transfer learning method and
modified method, transfer learning AlexNet showed the highest accuracy of 87.11%
while modified ResNet-18 demonstrated the lowest accuracy of 76.09%. Based on
observation of this project results and findings from other sources, for both approaches
taken for brain tumor image classification, the tuning of hyperparameters used in
training options (for transfer learning method only), the variation of image data
augmentation to avoid overfitting and the depth of CNN topology influences the
accuracy of the CNN models. The best CNN model for the classification of brain tumor
MRI images with great accuracy was proven to be transfer learning AlexNet model. |
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