Machine Learning and Deep Learning (Neural network) Approaches for Brain Tumor Detection and Classification: A Comprehensive Review

S.V. Viraktamath; Mahmad Sohel

doi:10.36948/ijfmr.2026.v08i02.74059

Machine Learning and Deep Learning (Neural network) Approaches for Brain Tumor Detection and Classification: A Comprehensive Review

Author(s)	Dr. S.V. Viraktamath, Mr. Mahmad Sohel
Country	India
Abstract	Brain tumor detection and classification using medical imaging has become a critical area of research in healthcare informatics. This comprehensive review examines recent advances in machine learning and deep learning methodologies applied to automated brain tumor detection and segmentation from magnetic resonance imaging (MRI) scans. Thirty state-of-the-art approaches are examined and organized into four categories: traditional machine learning, deep learning, transfer learning, and hybrid approaches. Traditional methods leveraging Support Vector Machines (SVM), Random Forests, and feature extraction techniques such as Discrete Wavelet Transform (DWT) and Principal Component Analysis (PCA) demonstrate effectiveness in smaller datasets. Deep learning approaches, particularly Convolutional Neural Networks (CNNs), U-Net architectures, and Capsule Networks, have shown superior performance in complex tumor segmentation tasks. Transfer learning using pre-trained models like ResNet, VGG, and Inception has proven particularly effective for limited medical datasets. This review identifies key challenges including dataset limitations, computational complexity, and generalization across different imaging protocols, while highlighting promising directions for future research in multimodal learning, explainable AI, and clinical deployment strategies.
Keywords	Brain tumor detection, MRI segmentation, deep learning, convolutional neural networks, transfer learning, machine learning, medical image analysis
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-10
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.74059

View / Download PDF File

E-ISSN 2582-2160

doi

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

IJFMR DOI prefix is
10.36948/ijfmr

Downloads

Research Paper Format Copyright Permission Form and Undertaking Form Cover Page Vol 8 Isu 2 Cover Page Vol 8 Isu 1 Cover Page Vol 7 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

About IJFMR 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		+91-9687-828-838	editor@ijfmr.com

International Journal For Multidisciplinary Research

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

Machine Learning and Deep Learning (Neural network) Approaches for Brain Tumor Detection and Classification: A Comprehensive Review

Share this