Hybrid deep neural network with clustering algorithms for effective gliomas segmentation

Akshya Kumar Sahoo, Priyadarsan Parida () and K. Muralibabu
Additional contact information
Akshya Kumar Sahoo: GIET University
Priyadarsan Parida: GIET University
K. Muralibabu: GIET University

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 3, No 12, 964-980

Abstract: Abstract Brain tumor detection is one of the most significant areas in the field of medical imaging. Gliomas are the most common primary malignant tumors in the brain. Accurate detection of Gliomas is a strenuous task due to their aggressiveness and diversified structure. In this paper, a hybrid Deep CNN is proposed which is created by a pre-trained Resnet101 embedded with 12 new layers. The proposed method is executed in three stages in the first stage, the hybrid deep CNN is trained by BraTs 2020 and BraTs 2017 multi-parametric MRI (mPMRI) dataset to detect the whole tumors. Secondly, after the detection of the whole tumor, the tumor core and the edema regions are segmented using the Local center of mass (LCM) method. In the third stage, K-means clustering is implemented for extracting the tumor core and edema regions. The proposed method is applied to BraTs 2020 and BraTs 2017 image datasets taken from the center of the biomedical image computing and analysis (CBICA) portal. The accuracies of the method are calculated as 99.06%, 99.23%, and 99.5% for whole tumor, tumor core, and edema respectively. The average dice scores calculated in our approach are 84.66%, 78.98%, and 72.79% for whole tumor, edema, and tumor core respectively.

Keywords: Gliomas; Resnet-101; LCM; K-means clustering (search for similar items in EconPapers)
Date: 2024
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-023-02183-w 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:3:d:10.1007_s13198-023-02183-w

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

DOI: 10.1007/s13198-023-02183-w

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 ().