An efficient deep learning approach for brain tumor detection using 3D convolutional neural network
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.
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| Langue: | English |
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Brac University
2023
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| Accès en ligne: | http://hdl.handle.net/10361/17925 |
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10361-179252023-03-22T05:37:28Z An efficient deep learning approach for brain tumor detection using 3D convolutional neural network Ali, Syed Muaz Alam, Dr. Md. Ashraful Department of Computer Science and Engineering, Brac University Deep learning approach Brain tumor detection 3D convolutional neural network Neural networks (Computer science) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-38). In medical application, deep learning-based biomedical pixel-wise detection through semantic segmentation has provided excellent results and proven to be efficient than manual segmentation by human interaction in various cases. A well-known and widely used architecture for biomedical segmentation is U-Net. In this work, a convolutional neural architecture based on 3D U-Net but with fewer parameters and lower computational cost is used for pixel-level detection of brain tumor through semantic segmentation. The proposed model is able to maintain a very efficient performance and provides better results in some cases compared to conventional U Net, while reducing memory usage, training time and inference time. BraTS 2021 dataset is used to evaluate the proposed architecture and it is able to achieve Dice scores of 0.9105 on Whole Tumor(WT), 0.884 on Tumor Core(TC) and 0.8254 on Enhancing-Tumor(ET) on the testing dataset. Syed Muaz Ali B. Computer Science 2023-02-28T06:16:36Z 2023-02-28T06:16:36Z 2022 2022-09 Thesis ID: 17201014 http://hdl.handle.net/10361/17925 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. 38 pages application/pdf Brac University |
| institution |
Brac University |
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Institutional Repository |
| language |
English |
| topic |
Deep learning approach Brain tumor detection 3D convolutional neural network Neural networks (Computer science) |
| spellingShingle |
Deep learning approach Brain tumor detection 3D convolutional neural network Neural networks (Computer science) Ali, Syed Muaz An efficient deep learning approach for brain tumor detection using 3D convolutional neural network |
| description |
This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022. |
| author2 |
Alam, Dr. Md. Ashraful |
| author_facet |
Alam, Dr. Md. Ashraful Ali, Syed Muaz |
| format |
Thesis |
| author |
Ali, Syed Muaz |
| author_sort |
Ali, Syed Muaz |
| title |
An efficient deep learning approach for brain tumor detection using 3D convolutional neural network |
| title_short |
An efficient deep learning approach for brain tumor detection using 3D convolutional neural network |
| title_full |
An efficient deep learning approach for brain tumor detection using 3D convolutional neural network |
| title_fullStr |
An efficient deep learning approach for brain tumor detection using 3D convolutional neural network |
| title_full_unstemmed |
An efficient deep learning approach for brain tumor detection using 3D convolutional neural network |
| title_sort |
efficient deep learning approach for brain tumor detection using 3d convolutional neural network |
| publisher |
Brac University |
| publishDate |
2023 |
| url |
http://hdl.handle.net/10361/17925 |
| work_keys_str_mv |
AT alisyedmuaz anefficientdeeplearningapproachforbraintumordetectionusing3dconvolutionalneuralnetwork AT alisyedmuaz efficientdeeplearningapproachforbraintumordetectionusing3dconvolutionalneuralnetwork |
| _version_ |
1814308557446709248 |