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Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis.

Overview

Authors: Usman Ali Khan1, Sahar Badri1, Syed Humaid Hasan2, Syeda Huyam Hasan3, Syed Hamid Hasan1
ORCID iDs: Sahar Badri
  1. Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
  2. Cloud Engineer, Department of Deployment, Amazon, USA
  3. Solutions Architect, Department of Data Insight Analytics, Amazon, USA
Institutions: King Abdulaziz University (Saudi Arabia); Amazon (United States) (United States)
Journal: Scientific reports, volume 16, issue 1, article 19178
Dates: received 11 January 2025; accepted 20 April 2026; published online 26 April 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-50213-x · PMID 42036459 · PMCID PMC13282374 · OpenAlex W7155718598
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Statistics, Machine learning, fMRI & imaging, Physiology & signal measures
Keywords: Multiclass Brain Tumor Classification, MRI-based Diagnostic Modeling, Ensemble Learning for Medical Imaging, CNN for image classification, SVM with RBF Kernel, ML-Enhanced MRI Classification, Brain imaging, CNS cancer
MeSH: Brain Neoplasms*, Machine Learning*, Neuroimaging*, Classification Algorithms, Convolutional Neural Networks, Glioma, Humans, Magnetic Resonance Imaging, Meningioma, Prognosis, Random Forest, Support Vector Machine (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Funding: King Abdulaziz University
Citations: not cited yet (Europe PMC); 51 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Code

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Data

Datasets cited

Data availability statement

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Read it in the paper: doi.org/10.1038/s41598-026-50213-x.

Versions

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 8 keywords, 12 MeSH terms, 1 funder, 22 references.

Cite

This paper

Khan, U. A., Badri, S., Hasan, S. H., Hasan, S. H., & Hasan, S. H. (2026). Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis. Scientific reports, 16(1), 19178. https://doi.org/10.1038/s41598-026-50213-x

BibTeX

@article{khan2026neuroimaging,
author = {Khan, Usman Ali and Badri, Sahar and Hasan, Syed Humaid and Hasan, Syeda Huyam and Hasan, Syed Hamid},
title = {{Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis}},
journal = {Scientific reports},
year = {2026},
month = apr,
volume = {16},
number = {1},
pages = {19178},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-50213-x},
url = {https://doi.org/10.1038/s41598-026-50213-x},
pmid = {42036459},
pmcid = {PMC13282374}
}

RIS

TY - JOUR
AU - Khan, Usman Ali
AU - Badri, Sahar
AU - Hasan, Syed Humaid
AU - Hasan, Syeda Huyam
AU - Hasan, Syed Hamid
TI - Neuroimaging and machine learning fusion for improved brain tumor diagnosis and prognosis
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/04/26
VL - 16
IS - 1
SP - 19178
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-50213-x
UR - https://doi.org/10.1038/s41598-026-50213-x
LA - en
ER -

CSL-JSON

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