Innovative brain tumor detection using optimized deep learning techniques

Praveen Kumar Ramtekkar (), Anjana Pandey () and Mahesh Kumar Pawar ()
Additional contact information
Praveen Kumar Ramtekkar: University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya
Anjana Pandey: University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya
Mahesh Kumar Pawar: University Institute of Technology, Rajiv Gandhi Proudyogiki Vishwavidyalaya

International Journal of System Assurance Engineering and Management, 2023, vol. 14, issue 1, No 39, 459-473

Abstract: Abstract An unusual increase of nerves inside the brain, which disturbs the actual working of the brain, is called a brain tumor. It has led to the death of lots of lives. To save people from this disease timely detection and the right cure is the need of time. Finding tumor-affected cells in the human brain is a cumbersome and time- consuming task. However, the accuracy and time required to detect brain tumors is a big challenge in the arena of image processing. This research paper proposes an innovative, accurate and optimized system to detect brain tumors. The system follows the activities like, preprocessing, segmentation, feature extraction, optimization and detection. The preprocessing system uses a compound filter, which is a composition of Gaussian, mean and median filters. Threshold and histogram techniques are applied for image segmentation. Grey level co- occurrence matrix is used for feature extraction. The optimized convolution neural network (CNN) technique is applied here that uses ant colony optimization, bee colony optimization and particle swarm optimization, genetic algorithm, gray wolf optimization and whale optimization algorithm techniques for best feature selection. Detection of brain tumors is achieved through CNN classifiers. This system compares its performance with another modern technique of optimization by using accuracy, precision and recall parameters and claims the supremacy of this work. This system is implemented in the Python programming language. The brain tumor detection accuracy of this optimized system has been measured at 98.9%.

Keywords: ACO; BCO; CNN; GA; GLCM; GWO; MRI; PSO; WOA (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s13198-022-01819-7 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:14:y:2023:i:1:d:10.1007_s13198-022-01819-7

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

DOI: 10.1007/s13198-022-01819-7

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 ().