Brain tumor image segmentation using model average ensembling of deep networks

Ajey Shakti Mishra, Upendra Kumar Acharya (), Akanksha Srivastava, Aashi Rohit Modi and Sandeep Kumar
Additional contact information
Ajey Shakti Mishra: Galgotias College of Engineering and Technology
Upendra Kumar Acharya: Galgotias College of Engineering and Technology
Akanksha Srivastava: Galgotias College of Engineering and Technology
Aashi Rohit Modi: Galgotias College of Engineering and Technology
Sandeep Kumar: National Institute of Technology

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 8, No 26, 3915-3925

Abstract: Abstract In the biomedical field, identification of brain tumors along with their location, regions of spreading, and speed of extension are of utmost importance to decide the treatment for Brain Tumors. Automated segmentation plays a major role in detection because manual extraction of the brain tumor sub-regions from MRI volume is monotonous, error-prone, and intricate. Deep learning significantly contributed to outperforming these issues since it is aware of their complexity. Therefore, a technique for the automated segmentation of MRI brain pictures has been developed using model average ensembling of deep networks such 3D CNN and U-Net architectures. 3D CNN and U-Net architecture have made remarkable progress on the task of segmentation of brain tumors. Due to their reliability, ensembling of these models have been opted to have a model with greater reliability. The novelty of this paper is to build a robust segmentation technique by model average ensembling of 3D CNN and U-Net models for abnormality identification by improving the image quality using preprocessing methods. The model includes the testing set BraTS-19 as its input dataset. After performing a lot of experiments, it has been observed that the obtained dice scores by the proposed model for TC (Tumor Core), WT (Whole Tumor), and ET (Enhancing Tumor) are 0.9603, 0.9201, and 0.9237 respectively. The obtained dice scores from the ensembling technique are better than existing techniques. The demonstrated results show the supremacy of the proposed method with an overall accuracy greater than 96%.

Keywords: Automated segmentation; Brain tumor; 3D CNN; U-Net; Deep learning; Ensembling (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02392-x Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:8:d:10.1007_s13198-024-02392-x

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02392-x

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().