A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor
Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-06-01
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| Series: | Frontiers in Oncology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fonc.2022.873268/full |
| _version_ | 1811245423743991808 |
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| author | Tahir Mohammad Ali Ali Nawaz Attique Ur Rehman Attique Ur Rehman Rana Zeeshan Ahmad Abdul Rehman Javed Thippa Reddy Gadekallu Chin-Ling Chen Chin-Ling Chen Chin-Ling Chen Chih-Ming Wu |
| author_facet | Tahir Mohammad Ali Ali Nawaz Attique Ur Rehman Attique Ur Rehman Rana Zeeshan Ahmad Abdul Rehman Javed Thippa Reddy Gadekallu Chin-Ling Chen Chin-Ling Chen Chin-Ling Chen Chih-Ming Wu |
| author_sort | Tahir Mohammad Ali |
| collection | DOAJ |
| description | Magnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS’20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively. |
| first_indexed | 2024-04-12T14:38:48Z |
| format | Article |
| id | doaj.art-53705147049b4d0c98f93f4da3e6d176 |
| institution | Directory Open Access Journal |
| issn | 2234-943X |
| language | English |
| last_indexed | 2024-04-12T14:38:48Z |
| publishDate | 2022-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Oncology |
| spelling | doaj.art-53705147049b4d0c98f93f4da3e6d1762022-12-22T03:28:55ZengFrontiers Media S.A.Frontiers in Oncology2234-943X2022-06-011210.3389/fonc.2022.873268873268A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain TumorTahir Mohammad Ali0Ali Nawaz1Attique Ur Rehman2Attique Ur Rehman3Rana Zeeshan Ahmad4Abdul Rehman Javed5Thippa Reddy Gadekallu6Chin-Ling Chen7Chin-Ling Chen8Chin-Ling Chen9Chih-Ming Wu10Department of Computer Science, GULF University for Science and Technology, Mishref, KuwaitDepartment of Computer Science, GULF University for Science and Technology, Mishref, KuwaitDepartment of Computer Science, GULF University for Science and Technology, Mishref, KuwaitDepartment of Software Engineering, University of Sialkot, Sialkot, PakistanDepartment of Information Technology, University of Sialkot, Sialkot, PakistanDepartment of Cyber Security, Air University, Islamabad, PakistanSchool of Information Technology and Engineering, Vellore Institute of Technology, Vellore, IndiaSchool of Information Engineering, Changchun Sci-Tech University, Changchun, ChinaSchool of Computer and Information Engineering, Xiamen University of Technology, Xiamen, ChinaDepartment of Computer Science and Information Engineering, Chaoyang University of Technology, Taichung, TaiwanSchool of Civil Engineering and Architecture, Xiamen University of Technology, Xiamen, ChinaMagnetic resonance imaging is the most generally utilized imaging methodology that permits radiologists to look inside the cerebrum using radio waves and magnets for tumor identification. However, it is tedious and complex to identify the tumorous and nontumorous regions due to the complexity in the tumorous region. Therefore, reliable and automatic segmentation and prediction are necessary for the segmentation of brain tumors. This paper proposes a reliable and efficient neural network variant, i.e., an attention-based convolutional neural network for brain tumor segmentation. Specifically, an encoder part of the UNET is a pre-trained VGG19 network followed by the adjacent decoder parts with an attention gate for segmentation noise induction and a denoising mechanism for avoiding overfitting. The dataset we are using for segmentation is BRATS’20, which comprises four different MRI modalities and one target mask file. The abovementioned algorithm resulted in a dice similarity coefficient of 0.83, 0.86, and 0.90 for enhancing, core, and whole tumors, respectively.https://www.frontiersin.org/articles/10.3389/fonc.2022.873268/fullVGG19UNETattention mechanismbrain tumor segmentationMRIBRATS |
| spellingShingle | Tahir Mohammad Ali Ali Nawaz Attique Ur Rehman Attique Ur Rehman Rana Zeeshan Ahmad Abdul Rehman Javed Thippa Reddy Gadekallu Chin-Ling Chen Chin-Ling Chen Chin-Ling Chen Chih-Ming Wu A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor Frontiers in Oncology VGG19 UNET attention mechanism brain tumor segmentation MRI BRATS |
| title | A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor |
| title_full | A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor |
| title_fullStr | A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor |
| title_full_unstemmed | A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor |
| title_short | A Sequential Machine Learning-cum-Attention Mechanism for Effective Segmentation of Brain Tumor |
| title_sort | sequential machine learning cum attention mechanism for effective segmentation of brain tumor |
| topic | VGG19 UNET attention mechanism brain tumor segmentation MRI BRATS |
| url | https://www.frontiersin.org/articles/10.3389/fonc.2022.873268/full |
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