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...
Saved in:
| Main Author: | |
|---|---|
| Format: | Thesis |
| Language: | English English English |
| Published: |
2021
|
| Subjects: | |
| 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 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| id |
my-uthm-ep.6999 |
|---|---|
| record_format |
uketd_dc |
| spelling |
my-uthm-ep.69992022-04-26T07:17:08Z A comparison study of deep CNN models for brain tumor MRI image classification 2021-07 Muhammad Imran, Mohd Ridzuan RC71-78.7 Examination. Diagnosis Including radiography 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. 2021-07 Thesis http://eprints.uthm.edu.my/6999/ http://eprints.uthm.edu.my/6999/1/24p%20MOHD%20RIDZUAN%20MUHAMMAD%20IMRAN.pdf text en public http://eprints.uthm.edu.my/6999/2/MOHD%20RIDZUAN%20MUHAMMAD%20IMRAN%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/6999/3/MOHD%20RIDZUAN%20MUHAMMAD%20IMRAN%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Kejuruteraan Elektrik dan Elektronik |
| institution |
Universiti Tun Hussein Onn Malaysia |
| collection |
UTHM Institutional Repository |
| language |
English English English |
| topic |
RC71-78.7 Examination Diagnosis Including radiography |
| spellingShingle |
RC71-78.7 Examination Diagnosis Including radiography Muhammad Imran, Mohd Ridzuan A comparison study of deep CNN models for brain tumor MRI image classification |
| description |
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. |
| format |
Thesis |
| qualification_name |
Master of Philosophy (M.Phil.) |
| qualification_level |
Master's degree |
| author |
Muhammad Imran, Mohd Ridzuan |
| author_facet |
Muhammad Imran, Mohd Ridzuan |
| author_sort |
Muhammad Imran, Mohd Ridzuan |
| title |
A comparison study of deep CNN models for brain tumor MRI image classification |
| title_short |
A comparison study of deep CNN models for brain tumor MRI image classification |
| title_full |
A comparison study of deep CNN models for brain tumor MRI image classification |
| title_fullStr |
A comparison study of deep CNN models for brain tumor MRI image classification |
| title_full_unstemmed |
A comparison study of deep CNN models for brain tumor MRI image classification |
| title_sort |
comparison study of deep cnn models for brain tumor mri image classification |
| granting_institution |
Universiti Tun Hussein Malaysia |
| granting_department |
Fakulti Kejuruteraan Elektrik dan Elektronik |
| publishDate |
2021 |
| url |
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 |
| _version_ |
1747831107014885376 |