A novel hybrid transformer-based framework (H-ConvNeXt-Swin) to classify brain tumors using MRI.

Overview

1. Faculty of Computer Science and Engineering, Alamein International University, New Alamein City, Matrouh Egypt 51718
2. Computers and Systems Department, Electronics Research Institute,Joseph Tito St, El Nozha, P.O. Box: 12622, Cairo, Cairo Governorate Egypt
3. Department of Computer Science and Engineering, Faculty of Computer Science and Engineering, New Mansoura University,New Mansoura, Egypt
4. Department of Electronic and Electrical Communication Engineering, Faculty of Electronic Engineering, Menoufia University,Menouf, Egypt

Abstract

This paper introduces a hybrid deep learning model combining ConvNeXt and Swin Transformer for classifying brain tumors from MRI scans. The ConvNeXt backbone is employed to obtain detailed local spatial features, whereas the Swin Transformer identifies hierarchical long-range dependencies, facilitating complementary feature representation. The proposed model is evaluated on a combined public MRI dataset of 7,023 images distributed across four categories: glioma, meningioma, pituitary, and no tumor. Experimental results demonstrate that the proposed hybrid architecture outperforms several state-of-the-art convolutional and transformer-based models, achieving an accuracy of 95.37% with competitive precision and F-score. Additionally, qualitative explainability assessment using attention-based visualization techniques offers insight into the model’s decision-making by highlighting diagnostically significant regions. Furthermore, we evaluate the proposed H-ConvNeXt-Swin model on the unified dataset and also report source-stratified performance on each of the three constituent datasets: Figshare, SARTAJ, and Br35H. Future work will focus on validating the proposed framework on multi-center clinical datasets and extending it to more complex tasks such as tumor localization and segmentation.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

The paper says that its authors' code is available on request: it was not published with the paper, so there is nothing to verify.

Code availability

The proposed hybrid ConvNeXt–Swin Transformer framework, along with the training and evaluation scripts, has not yet been made available as an open repository. However, the source code can be made available upon a reasonable request to the corresponding author for non-commercial academic research purposes, to facilitate reproducibility and additional exploration.

Reproduced under the paper's license (CC BY), from the paper cited above.

Tracing map

A tracing map links a paper to the code its authors published: this paper has none (its code is available on request), so it has no map.

Data

Datasets cited

• kaggle.com/datasets/masoudnickparvar, at Kaggle; found in “Data availability”

Data availability

The Brain Tumor MRI Dataset analyzed during the current study are available in the Kaggle repository: https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset?resource=download.

Reproduced under the paper's license (CC BY), from the paper cited above.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 4 authors, 13 keywords, 7 MeSH terms, 1 funder, 14 references.

Cite

This paper

Abdellatef, E., Al-Makhlasawy, R. M., El-Mawla, N. A., & Shalaby, W. A. (2026). A novel hybrid transformer-based framework (H-ConvNeXt-Swin) to classify brain tumors using MRI. Scientific reports, 16(1), 24168. https://doi.org/10.1038/s41598-026-63915-z

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:10.1038/s41598-026-52658-6

A multi-level attention CNN-transformer based framework for the detection of brain tumor using regional dual-score explainability.

Journal: Scientific reports

In common: kaggle.com/datasets/masoudnickparvar, methods / tools, structural MRI / diffusion, other condition, 1 reference

[2] doi:10.1371/journal.pone.0346821

LHW-Net: An ensemble-based machine learning framework for brain tumor classification.

Journal: PloS one

In common: kaggle.com/datasets/masoudnickparvar, methods / tools, other condition, 1 reference

[3] doi:10.1038/s41598-026-50213-x

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

Journal: Scientific reports

In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 1 reference

[4] doi:10.1002/acm2.70560 [code]

Hybrid CNN-GCN framework for brain tumor MRI classification: A graph-based approach to smart healthcare diagnostics.

Journal: Journal of applied clinical medical physics

In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 1 reference

[5] doi:10.3389/frai.2026.1849051

Query-guided learning for efficient and interpretable multi-class brain tumor classification in MRI.

Journal: Frontiers in artificial intelligence

In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 1 reference

[6] doi:10.3389/fnins.2026.1875642

Convolutional neural networks in brain disease diagnosis: a unified review of Alzheimer's, Parkinson's, and brain tumor classification.

Journal: Frontiers in neuroscience

In common: kaggle.com/datasets/masoudnickparvar, structural MRI / diffusion, other condition, 1 reference

[7] doi:10.1371/journal.pone.0344291

Deep learning based two-way feature depiction model for brain tumor detection.

Journal: PloS one

In common: kaggle.com/datasets/masoudnickparvar, methods / tools, structural MRI / diffusion, other condition

[8] doi:10.1038/s41598-026-48825-4

YOLO-LS: a novel deep learning framework for brain tumor segmentation in Magnetic Resonance Imaging.

Journal: Scientific reports

In common: kaggle.com/datasets/masoudnickparvar, methods / tools, structural MRI / diffusion, other condition

[9] doi:10.3390/s26092822

Multi-Chaotic HEOA for Hardware-Aware Neural Architecture Search: Brain Tumor Classification on FPGA.

Journal: Sensors (Basel, Switzerland)

In common: kaggle.com/datasets/masoudnickparvar, methods / tools, structural MRI / diffusion, other condition

[10] doi:10.1038/s41598-026-44957-9

Multimodal deep feature fusion with transformer for brain tumor classification from magnetic resonance imaging.

Journal: Scientific reports

In common: kaggle.com/datasets/masoudnickparvar, methods / tools, structural MRI / diffusion, other condition

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.

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: 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.