Advanced International Journal for Research

E-ISSN: 3048-7641   •   Impact Factor: 9.11

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 7, Issue 3 (May-June 2026) Submit your research before last 3 days of June to publish your research paper in the issue of May-June.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 7 Issue 3
May-June 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar Wiki for Call for Papers WorldCat Zenodo

Deep Learning Techniques for MRI-Based Brain Tumor Analysis: A Comprehensive Review

Author(s) Vidhita S. Kamble, Prof. Monika Ingole, Prof. Shrunkhala Raut
Country India
Abstract Brain tumors are one of the most serious neurological diseases that need an early and correct diagnosis to make the righttreatment plan and consequently increase the survival of the patient. The latest breakthroughs in MRI have led to theacquisition of the images of brain structures at a very high resolution but still the manual analysis of these images is a verylong, subjective, and error-prone process. The deep learning (DL) methods have been introduced as a very effective tool forautomatically detecting, segmenting, and classifying brain tumors in such case. This review gives a thorough discussion ofthe latest studies (2023-2025) utilizing deep learning methods on the analysis of brain tumors through MRI. It details
convolutional neural networks, U-Net family variations, models based on Transformer architecture and hybrid networkstailored for tumor detection, grading, segmentation, and prognosis prediction. The public benchmark datasets like BraTS andmulti-institutional MRI datasets are also discussed. Though very high accuracies have been reported, the studies revealchallenges such as the models being very limited in their generalization abilities, heterogeneous data, lack of interpretedresults, and no thorough clinical validation. This review highlights the main trends, pros and cons, and at the same time, itpoints out the unexplored questions in research direction for the next generation of brain tumor diagnosis systems that will be
solid, understandable, and usable in the clinic.
Keywords Brain Tumor Detection, Deep Learning, MRI Image Analysis, Tumor Segmentation, Computer-Aided Diagnosis etc.
Published In Volume 7, Issue 3, May-June 2026
Published On 2026-05-27
DOI https://doi.org/10.63363/aijfr.2026.v07i03.5762
View / Download PDF File

Share this



E-ISSN 3048-7641
CrossRef

CrossRef DOI is assigned to each research paper published in our journal.

AIJFR DOI prefix is
10.63363/aijfr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 7 Isu 2Cover Page Vol 7 Isu 1Cover Page Vol 6 Isu 6

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About AIJFR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9898-79-39-59 editor@aijfr.com