S 2 A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images.

Overview

1. Ramrao Adik Institute of Technology, Navi Mumbai, India
2. Lokmanya Tilak College of Engineering, Koparkhairane, Navi Mumbai, India
3. Department of Information Technology, Vidhyalankar Institute of Technology, Wadala, Mumbai, India
4. Department of Artificial Intelligence and Machine Learning, Manipal University Jaipur, Jaipur, 303007 Rajasthan India

Abstract

Globally, the main factor that contributes to increasing the mortality rate among people is the development of abnormal cells in the brain, which leads to a Brain Tumor (BT). Therefore, the classification of BT is essential to prevent the increasing death rate by diagnosing the tumor based on its type. In order to classify the types of BT, several models are introduced, but they possess numerous drawbacks, including poor accuracy, higher time consumption, computational complexities, overfitting, and so forth. Hence, the Standalone Self-Attention based Repeated Convolutional Network (S2A-RConvNet) model is developed to classify the BT types accurately to save the lives of affected people by solving the limitations of conventional approaches. The incorporation of the Standalone Self-Attention (S2A) module enables the RConvNet to focus more on the tumor area, which helps to increase the model’s accuracy in BT categorization. Furthermore, the extraction of Structured ResNet Attention Gray-level (SRAG) features increases the training period and decreases the computational complexities, which leads to better performance of the model in BT classification. The S2A-RConvNet model attained the values of sensitivity of 97.61%, precision of 98.71%, F1-Score of 98.16%, specificity of 98.43% and accuracy of 97.98% with 90% of training using the BraTS 2021 dataset.

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

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

• kaggle.com/datasets/abdullahalmunem, at Kaggle; found in the references
• kaggle.com/datasets/dschettler8845, at Kaggle; found in “Data availability”

Data availability

The datasets analyzed during the current study are available in the BRaTS 2021 Task 1 Dataset, (https:/www.kaggle.com/datasets/dschettler8845/brats-2021-task1/data), and BraTS 2017 dataset, [https://www.kaggle.com/datasets/abdullahalmunem/brats17].

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, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 7 authors, 4 keywords, 5 MeSH terms, 1 funder, 11 references.

Cite

This paper

Waghmode, U., Naik, A., Deone, J., Choudhury, S. R., Dhawale, D., Puri, D., & Solanki, S. (2026). S 2 A-RConvNet: standalone self-attention enabled deep learning model for brain tumor classification with MRI images. Scientific reports, 16(1), 19143. https://doi.org/10.1038/s41598-026-46010-1

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:10.3389/fonc.2026.1928589

LiteFreqMamba:lightweight frequency-enhanced Mamba for efficient 3D brain tumor segmentation.

Journal: Frontiers in oncology

In common: kaggle.com/datasets/dschettler8845, other condition

[2] 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: structural MRI / diffusion, other condition, 1 reference

[3] doi:10.1038/s41598-026-47044-1

MRI-based brain tumor prediction using convolutional neural network framework.

Journal: Scientific reports

In common: structural MRI / diffusion, other condition, 1 reference

[4] doi:10.1038/s41598-026-60334-y

Enhancing brain tumor detection through deep learning and explainable AI techniques.

Journal: Scientific reports

In common: structural MRI / diffusion, other condition, 1 reference

[5] doi:10.2196/84095

Detection of Interpretable and Fine-Grained Brain Tumor Magnetic Resonance Imaging Based on Progressive Pruning: Machine Learning Model Development and Validation Study.

Journal: JMIR medical informatics

In common: structural MRI / diffusion, other condition, 1 reference

[6] doi:10.3389/fninf.2026.1795354

A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI.

Journal: Frontiers in neuroinformatics

In common: structural MRI / diffusion, other condition, 1 reference

[7] 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: structural MRI / diffusion, other condition, 1 reference

[8] 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: structural MRI / diffusion, other condition, 1 reference

[9] 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: structural MRI / diffusion, other condition, 1 reference

[10] doi:10.3389/frai.2026.1849571

NeuroPlast: a learnable activation function evaluated under knowledge distillation for medical image classification.

Journal: Frontiers in artificial intelligence

In common: other condition, 1 reference

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.