In-depth analysis of deep learning architectures for brain tumor classification in MRI scans
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024.
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Brac University
2024
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| Online Zugang: | http://hdl.handle.net/10361/24166 |
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10361-241662024-09-25T05:38:09Z In-depth analysis of deep learning architectures for brain tumor classification in MRI scans Haque, Hossain MD. Hasibul Apon, MD. Sayeed Arefin Chowdhury, Dhrubo Rashid Imtiaz, Shahriar Islam Mahi, Nishat Tasnim Karim, Dewan Ziaul Ziaul, Dewan Alam, Golam Rabiul Department of Computer Science and Engineering, Brac University Brain tumor CNN MRI Diagnosis Deep learning. Magnetic resonance imaging. Brain tumors--Diagnosis. This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. Cataloged from PDF version of thesis. Includes bibliographical references (pages no. 45-47). One of the deadliest and most difficult tumors to cure is a brain tumor. Patients diagnosed with brain tumors tend to have a comparatively shorter lifespan. This tumor can affect any individual of any age. To mitigate the damages of brain tumors, early prognosis, and diagnosis are mandatory for a comparatively longer lifespan. Our primary goal is to develop a functional convolutional neural network (CNN) model that can reliably identify brain tumor cells in a patient’s magnetic resonance imaging (MRI). Unfortunately, this is a hard task as there are not many resources available as around 2 to 3 cases occur each year in 100,000 individuals in Bangladesh. For this purpose, a dataset was collected and augmented into a larger dataset by splitting, rotating, changing orientation, etc. Three categories were added to the dataset: training, validation, and testing where 70% of the data was for training, 15% for validation, and 15% for testing. Finally, we trained our dataset for 50 epochs to get the accuracy rate and then tested the same data sets with other pre-trained models like MobileNetV2, DenseNet121, and ResNet50. In this course of training our custom CNN model, we gained the highest accuracy rate, which is 97.07% in training, 95.99% in validation, and 96.51% for testing. Hossain MD. Hasibul Haque MD. Sayeed Arefin Apon Dhrubo Rashid Chowdhury Shahriar Islam Imtiaz Nishat Tasnim Mahi B.Sc. in Computer Science 2024-09-23T08:40:14Z 2024-09-23T08:40:14Z ©2024 2024 Thesis ID 18101656 ID 18201010 ID 20101278 ID 20101279 ID 18201044 http://hdl.handle.net/10361/24166 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 58 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Brain tumor CNN MRI Diagnosis Deep learning. Magnetic resonance imaging. Brain tumors--Diagnosis. |
| spellingShingle |
Brain tumor CNN MRI Diagnosis Deep learning. Magnetic resonance imaging. Brain tumors--Diagnosis. Haque, Hossain MD. Hasibul Apon, MD. Sayeed Arefin Chowdhury, Dhrubo Rashid Imtiaz, Shahriar Islam Mahi, Nishat Tasnim In-depth analysis of deep learning architectures for brain tumor classification in MRI scans |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2024. |
| author2 |
Karim, Dewan Ziaul |
| author_facet |
Karim, Dewan Ziaul Haque, Hossain MD. Hasibul Apon, MD. Sayeed Arefin Chowdhury, Dhrubo Rashid Imtiaz, Shahriar Islam Mahi, Nishat Tasnim |
| format |
Thesis |
| author |
Haque, Hossain MD. Hasibul Apon, MD. Sayeed Arefin Chowdhury, Dhrubo Rashid Imtiaz, Shahriar Islam Mahi, Nishat Tasnim |
| author_sort |
Haque, Hossain MD. Hasibul |
| title |
In-depth analysis of deep learning architectures for brain tumor classification in MRI scans |
| title_short |
In-depth analysis of deep learning architectures for brain tumor classification in MRI scans |
| title_full |
In-depth analysis of deep learning architectures for brain tumor classification in MRI scans |
| title_fullStr |
In-depth analysis of deep learning architectures for brain tumor classification in MRI scans |
| title_full_unstemmed |
In-depth analysis of deep learning architectures for brain tumor classification in MRI scans |
| title_sort |
in-depth analysis of deep learning architectures for brain tumor classification in mri scans |
| publisher |
Brac University |
| publishDate |
2024 |
| url |
http://hdl.handle.net/10361/24166 |
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