[email protected] +91 9840521421 Scholarly Open Access, Peer-reviewed, Refereed Journal e-ISSN 2582-8746 Since 2020
  1. Home
  2. Archives
  3. 2025
  4. Vol. 6 Issue 6
  5. Article
Original Article

Optimized Brain Tumor Detection: A Dual-Module Approach for MRI Image Enhancement and Tumor Classification

  • Paper ID 1008
  • Volume 6, Issue 6
  • Pages 59-66
  • Published Online: November-December 2025
  • 3 Views
Open Access Peer Reviewed Crossref DOI

Authors

  • Dr. B Harichandana1
  • Karanth J2
  • Dattatri3
  • Bhargav C4

1 2 3 4 Department of Computer Science and Engineering, Rajarajeswari College of Engineering, Bangalore, Karnataka, India.

Article Information

Published Online
November-December 2025
Pages
59-66
Article Type
Original Article
Check for updates
check update

Abstract

View PDF

A brain tumor diagnosis is one of the most difficult challenges a person can face. While MRI scans are incredible tools for seeing inside the brain, reading them takes time and a high level of precision. To help doctors provide faster, more accurate answers, we’ve developed a new automated system. By "cleaning up" digital images to make them clearer and using smart technology to spot abnormalities, our goal is to take the guesswork out of the process. This isn’t just about better data; it’s about giving patients and their families the clarity and quick action they deserve. Doctors rely on MRIs to find brain tumors, but even the best scans can sometimes be grainy or difficult to read. We created a framework that acts like a "high-definition" filter for medical imaging. First, it scrubs away digital noise and balances the lighting, making tumors stand out from healthy tissue. Then, a trained computer model double-checks the image for signs of illness.

APC from ₹1200 + 18% GST

Peer-reviewed open access₹1200 Indian (₹1400 with DOI)$80 USD internationalpaid after acceptance Peer-reviewed open access₹1200 Indian$80 USD international ₹1200 Indian · $80 intl

  • CrossRef DOI
  • ISSN 2582-8746
  • Open Access
  • Peer Reviewed
  • Plagiarism Checked
  • Google Scholar
Submit Paper