AccScience Publishing / IJOSI / Volume 8 / Issue 2 / DOI: 10.6977/IJoSI.202406_8(2).0008
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A novel hybrid deep belief Google network framework for brain tumor classification

Sanjeet Kumar1* ,  Urmila Pilania1 ,  Rajni Bala2
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1 Department of Computer Science and TechnologyManav Rachna UniversityFaridabad, Haryana-121004, India
2 Department of Computer ScienceDeen Dayal Upadhyaya College, University of DelhiSector-3, Dwarka, New Delhi-110078, India
IJOSI 2024 , 8(2), 95–112; https://doi.org/10.6977/IJoSI.202406_8(2).0008
Submitted: 23 January 2024 | Revised: 27 March 2024 | Accepted: 15 April 2024 | Published: 1 June 2024
© 2024 by the Author(s). Licensee AccScience Publishing, USA. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC BY-NC 4.0) ( https://creativecommons.org/licenses/by-nc/4.0/ )
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Abstract

A brain tumor can lead to headaches, seizures, numbness or weakness in the arms or legs, changes in personality or behavior, nausea, vomiting, visual or hearing disturbances, or dizziness. Brain tumors are lumps or atypical expansions of cells that developin the brain or central spinal canal. The current study used neural networks to identify with categorize brain tumors, although it has the drawbacks of longer delays in processing, overfitting, and exploding gradient. This study proposed a unique Hybrid Deep Belief Google Network (DBGN) system for brain tumor identi-fication and categorization in order to get over the constraints. Pre-processing and feature extraction with categoriza-tion are the components of this method. Utilizing the proposed Modified Global Contrast Stretching (MGCS), Hybrid Median and Wiener Filter (HMWF), and Modified Scharr operator to first pre-process the obtained pictures. Next, a fresh, efficient neural network was proposed by this study to gather and classify the brain tumor. As aresult, the method we propose outperforms the existing approaches in relation to accuracy, precision, recall, and specificity.

Keywords
Brain tumor
Medical imaging
Magnetic Resonance Imaging (MRI)
tumor classification
deep learn-ing
neural network.
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International Journal of Systematic Innovation, Electronic ISSN: 2077-8767 Print ISSN: 2077-7973, Published by AccScience Publishing
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