OSCR

MANet: a multimodal attention convolutional neural network for brain tumor classification.

Overview

Authors: Aaluri Seenu1, Kiran Kumar Eepuri2, B Siva Prasad3, K. Ch. Sri Kavya2, Sk Hasane Ahammad2, Wallaaldin Eltayeb4, Mustafa SirElkhatim5
ORCID iDs: Aaluri Seenu, Wallaaldin Eltayeb
  1. Department of CSE, Shri Vishnu Engineering College for Women, Bhimavaram, India
  2. Department of ECE, Koneru Lakshmaiah Education Foundation,Guntur, AP India
  3. Department of ECE, Nadimpalli Satyanarayana Raju Institute of Technology, Visakhapatnam, AP India
  4. Department of EEE, Koneru Lakshmaiah Education Foundation,Vaddeswaram, 522302 India
  5. Department of Chemical Engineering, Red Sea University,Port Sudan, 3315 Sudan
Institutions: Koneru Lakshmaiah Education Foundation (India); Red Sea University (Sudan)
Journal: Scientific reports, volume 16, issue 1, article 22787
Dates: received 7 March 2026; accepted 6 May 2026; published online 19 May 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41598-026-52615-3 · PMID 42156952 · PMCID PMC13385786 · OpenAlex W7161631359
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: structural MRI / diffusion (modality), human (organism), other condition (population)
Methods: Machine learning
Keywords: Brain tumor classification, Multimodal attention, Convolutional neural network, Wavelet attention, Edge attention, Texture attention, Cancer, Computational biology and bioinformatics, Engineering, Mathematics and computing, Medical research, Oncology
MeSH: Brain Neoplasms*, Classification Algorithms, Convolutional Neural Networks, Glioma, Humans, Image Processing, Computer-Assisted, Magnetic Resonance Imaging, Meningioma, Neural Networks, Computer, Tomography, X-Ray Computed (* major topic)
Topic: Brain Tumor Detection and Classification (Neurology, Neuroscience), according to OpenAlex
Citations: not cited yet (Europe PMC); 70 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

The paper links to its data, not to its authors' code: see the Data section.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none, so it has no map.

Data

Datasets cited

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

Read it in the paper: doi.org/10.1038/s41598-026-52615-3.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 28 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 12 keywords, 10 MeSH terms, 39 references.

Cite

This paper

Seenu, A., Eepuri, K. K., Prasad, B. S., Kavya, K. C. S., Ahammad, S. H., Eltayeb, W., & SirElkhatim, M. (2026). MANet: a multimodal attention convolutional neural network for brain tumor classification. Scientific reports, 16(1), 22787. https://doi.org/10.1038/s41598-026-52615-3

BibTeX

@article{seenu2026manet,
author = {Seenu, Aaluri and Eepuri, Kiran Kumar and Prasad, B Siva and Kavya, K. Ch. Sri and Ahammad, Sk Hasane and Eltayeb, Wallaaldin and SirElkhatim, Mustafa},
title = {{MANet: a multimodal attention convolutional neural network for brain tumor classification}},
journal = {Scientific reports},
year = {2026},
month = may,
volume = {16},
number = {1},
pages = {22787},
publisher = {Nature Publishing Group},
issn = {2045-2322},
doi = {10.1038/s41598-026-52615-3},
url = {https://doi.org/10.1038/s41598-026-52615-3},
pmid = {42156952},
pmcid = {PMC13385786}
}

RIS

TY - JOUR
AU - Seenu, Aaluri
AU - Eepuri, Kiran Kumar
AU - Prasad, B Siva
AU - Kavya, K. Ch. Sri
AU - Ahammad, Sk Hasane
AU - Eltayeb, Wallaaldin
AU - SirElkhatim, Mustafa
TI - MANet: a multimodal attention convolutional neural network for brain tumor classification
T2 - Scientific reports
J2 - Sci Rep
PY - 2026
DA - 2026/05/19
VL - 16
IS - 1
SP - 22787
SN - 2045-2322
PB - Nature Publishing Group
DO - 10.1038/s41598-026-52615-3
UR - https://doi.org/10.1038/s41598-026-52615-3
LA - en
ER -

CSL-JSON

{
"id": "10.1038/s41598-026-52615-3",
"type": "article-journal",
"title": "MANet: a multimodal attention convolutional neural network for brain tumor classification",
"container-title": "Scientific reports",
"author": [
{
"family": "Seenu",
"given": "Aaluri"
},
{
"family": "Eepuri",
"given": "Kiran Kumar"
},
{
"family": "Prasad",
"given": "B Siva"
},
{
"family": "Kavya",
"given": "K. Ch. Sri"
},
{
"family": "Ahammad",
"given": "Sk Hasane"
},
{
"family": "Eltayeb",
"given": "Wallaaldin"
},
{
"family": "SirElkhatim",
"given": "Mustafa"
}
],
"container-title-short": "Sci Rep",
"volume": "16",
"issue": "1",
"page": "22787",
"DOI": "10.1038/s41598-026-52615-3",
"PMID": "42156952",
"PMCID": "PMC13385786",
"ISSN": "2045-2322",
"publisher": "Nature Publishing Group",
"URL": "https://doi.org/10.1038/s41598-026-52615-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
19
]
]
}
}

Similar papers

The papers with a page that share the most with this one: the tools found in their code, their categories, datasets, cited references and authors, the rarest counting most.

[1] doi:10.3390/jimaging12060233 [code]
Brain Tumor Classification in MRI Images Using Combined Transfer Learning and Convolutional Neural Networks.
Journal: Journal of imaging
In common: figshare 1512427, kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 2 references
[2] doi:10.3390/brainsci16050468
Advancing Brain Tumor Diagnosis Using Deep Learning: A Systematic and Critical Review on Methodological Approaches to Glioma Segmentation and Classification Through Multiparametric MRI.
Journal: Brain sciences
In common: figshare 1512427, kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 2 references
[3] doi:10.1186/s40708-026-00325-x
CAE-BrainNet: a statistically validated class-adaptive attention ensemble model for explainable brain tumor classification from MRI.
Journal: Brain informatics
In common: figshare 1512427, kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 1 reference
[4] doi:10.3389/fonc.2026.1834400
BrainFusionNet: an attention-augmented deep convolutional framework with hybrid loss optimisation and test-time augmentation for multi-class brain tumour detection in magnetic resonance images.
Journal: Frontiers in oncology
In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 3 references
[5] doi:10.1038/s41598-026-43616-3
MM FD ConvFormer multimodal frequency aware deformable CNN transformer network for robust brain tumor classification.
Journal: Scientific reports
In common: figshare 1512427, kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition
[6] doi:10.3390/diagnostics16111745
Brain Tumor Classification and Segmentation in MR Images Using EfficientNet and U-Net++ Models.
Journal: Diagnostics (Basel, Switzerland)
In common: figshare 1512427, kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition
[7] doi:10.3390/diagnostics16152407
BG-YOLO11s: Boundary-Guided YOLO11 with Bézier Contour Augmentation for Brain Tumour Segmentation in T1-CE MRI.
Journal: Diagnostics (Basel, Switzerland)
In common: figshare 1512427, structural MRI / diffusion, other condition, 2 references
[8] doi:10.1371/journal.pone.0346821
LHW-Net: An ensemble-based machine learning framework for brain tumor classification.
Journal: PloS one
In common: figshare 1512427, kaggle.com/datasets/masoudnickparvar, other condition
[9] doi:10.1371/journal.pone.0352353
Deep convolutional GAN and hypernet-based neural architecture search for brain tumor diagnosis detection and classification.
Journal: PloS one
In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 2 references
[10] doi:10.1038/s41598-026-61891-y
A class-wise quantum relational calibration network for brain tumor diagnosis.
Journal: Scientific reports
In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 2 references

Contribute

The authors of this paper can claim it, correct its record and validate its tracing map, and the maintainers of its code (its owner, or a public member of its organization) correct what it says of their repository; anyone signed in can ask for its removal. Every request goes to OSCR's own machine, which answers it; your account page follows them.

Sign in with ORCID to claim this paper as one of its authors, correct its record or validate its tracing map: when the paper's metadata lists your ORCID iD, you are recognized at once. Maintainers of its code: sign in with GitHub, then claim the repository on your account page.

Request its removal

To ask OSCR to remove this record, the copies of its authors' scripts or its tracing map, use the removal request page: signed in, you say who you are, what to remove and why, then review and confirm the request. Published rules decide every request (how).

Discussion, reproductions, activity

Discussion: questions and error reports about this paper and its code, from signed-in readers and its authors. It opens with sign-in.

Reproductions: reports from readers who ran the authors' code: what they reproduced, with which environment, commit and data. It opens with sign-in.

Activity: what happens around this paper: new versions of its record, its map's validation, discussions and reproductions. It opens with sign-in.