---
title: "masoudnickparvar/brain-tumor-mri-dataset: papers citing it"
description: "14 papers in the tracked journals link to the Kaggle dataset masoudnickparvar/brain-tumor-mri-dataset in their Crossref records, the first in 2024; most of them (7) in 2025."
url: https://scholar4ai.com/code/kaggle-masoudnickparvar-brain-tumor-mri-dataset
markdown_url: https://scholar4ai.com/code/kaggle-masoudnickparvar-brain-tumor-mri-dataset.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "14 papers in the tracked journals link to the Kaggle dataset masoudnickparvar/brain-tumor-mri-dataset in their Crossref records, the first in 2024; most of them (7) in 2025. (as of 2026-11-03)"
data_url: https://scholar4ai.com/data/rankings.json
updated: 2026-11-03T14:43:26+09:00
window: 2026-10-05 to 2026-11-03
source: OpenAlex (CC0)
---

# masoudnickparvar/brain-tumor-mri-dataset: papers linking to it

Research papers in the 480 tracked journals that link to masoudnickparvar/brain-tumor-mri-dataset (https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset), read from their Crossref records: by year, category and journal, the family tree of papers building on one another, and the linked repositories derived from it or that it derives from.

- Kaggle
- https://www.kaggle.com/datasets/masoudnickparvar/brain-tumor-mri-dataset

All repositories: https://scholar4ai.com/code.md

## Key findings

- 14 papers in the tracked journals link to the Kaggle dataset masoudnickparvar/brain-tumor-mri-dataset in their Crossref records, the first in 2024; most of them (7) in 2025.
- By category: AI (14); the journals with the most are Neural Computing and Applications (7), Knowledge-Based Systems (4) and Expert Systems with Applications (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links masoudnickparvar/brain-tumor-mri-dataset.

## By year

*Papers linking to masoudnickparvar/brain-tumor-mri-dataset by publication year*

| Year | Papers |
|---|---:|
| 2024 | 1 |
| 2025 | 7 |
| 2026 | 6 |

## Journals

*Journals whose papers link to masoudnickparvar/brain-tumor-mri-dataset most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 7 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | 4 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |
| [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | 1 |
| [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [Brain tumor diagnosis using hybrid quantum-classical neural networks](https://doi.org/10.1007/s00521-025-11601-y) (Neural Computing and Applications, 2025)
  - [RViT-FusionNet: A local cross-attention feature fusion-based hybrid framework for brain tumor classification](https://doi.org/10.1007/s00521-026-12290-x) (Neural Computing and Applications, 2026) — cites the paper above

## Papers

*Papers linking to masoudnickparvar/brain-tumor-mri-dataset, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2024-02-21 | [VITALT: a robust and efficient brain tumor detection system using vision transformer with attention and linear transformation](https://doi.org/10.1007/s00521-023-09306-1) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | S. Poornam, Jeyaraj Jane Rubel Angelina | 34 | cites it |
| 2025-05-21 | [HMDFF-Net: Hierarchical multi-scale dilated feature fusion network for accurate multiclass diagnosis of brain tumors](https://doi.org/10.1016/j.eswa.2025.128272) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Sohaib Asif, Enyu Wang, Vicky Yang Wang et al. | 13 | cites it |
| 2025-10-13 | [Dual-functional fractal-fractional Sobel operator for efficient image enhancement and edge detection](https://doi.org/10.1016/j.patcog.2025.112592) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | K. Gowtham, N. Ramesh Babu, Shanmugam Harshavarthini et al. | 12 | cites it |
| 2025-10-24 | [Multi-teacher knowledge distillation and ensemble algorithm for efficient brain tumor classification in resource-constrained environments with explainable AI](https://doi.org/10.1016/j.knosys.2025.114711) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Md. Samiul Alim, Mahir Shahriar Tamim, Shuvo Sarkar et al. | 9 | cites it |
| 2025-09-08 | [Brain tumor diagnosis using hybrid quantum-classical neural networks](https://doi.org/10.1007/s00521-025-11601-y) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Deepak Ranga, Sunil Prajapat, Kranti Kumar et al. | 5 | cites it |
| 2025-10-10 | [Enhanced magnetic resonance imaging feature extraction for precise brain tumor classification using dual deep convolutional networks](https://doi.org/10.1016/j.knosys.2025.114628) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Denis Bernard, Constantino Msigwa, Jaeseok Yun | 5 | cites it |
| 2025-08-11 | [Multi-modal MRI data augmentation and attention-based modeling for interpretable brain tumor classification](https://doi.org/10.1007/s00521-025-11561-3) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Mubarak A. Alanazi | 4 | cites it |
| 2026-01-20 | [FreqMambaMark: Wavelet-Mamba-driven robust medical image watermarking](https://doi.org/10.1016/j.knosys.2026.115344) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Zhongxiang He, Yuling Chen, Yixian Yang et al. | 3 | cites it |
| 2025-06-18 | [Automated brain tumor detection using novel hybrid vision transformer and EfficientNetb4 models with comparative analysis](https://doi.org/10.1007/s00521-025-11335-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | S. Selva Karthik, V. Achuthan, Antony Dennis Ananth et al. | 2 | cites it |
| 2026-04-01 | [Towards reliable medical unsupervised anomaly detection: A benchmark and dataset for PET/CT with cross-modality knowledge distillation network](https://doi.org/10.1016/j.inffus.2026.104341) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Muhao Xu, Lili Qu, Zihan Nie et al. | 1 | cites it |
| 2026-02-01 | [Novel CNN-driven model for brain tumor image categorization](https://doi.org/10.1007/s00521-025-11770-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Essam Abdellatef, Alshimaa H. Ismail, Rasha M. Al‐Makhlasawy | 0 | cites it |
| 2026-05-02 | [Enhanced brain tumour detection from MRI images utilizing interpretable generalized additive neural network optimized with parrot optimization](https://doi.org/10.1016/j.knosys.2026.116131) | [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.md) | Ms. S. Sandhya, M. V. Chaitanya Kumar, S. K. Susee | 0 | cites it |
| 2026-05-27 | [Distinguished approach for brain tumor segmentation and classification using U-net and customized convolution neural network in magnetic resonance images](https://doi.org/10.1007/s00521-026-12105-z) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | S. Karkuzhali, S. Bhuvan Raaj, B. Hariharakrishnan | 0 | cites it |
| 2026-06-01 | [RViT-FusionNet: A local cross-attention feature fusion-based hybrid framework for brain tumor classification](https://doi.org/10.1007/s00521-026-12290-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Naima Islam, Sajeeb Kumar Ray, Md. Anwar Hossain et al. | 0 | cites it |

## How this is measured

Links are read from each paper's Crossref record as its publisher deposited it: the URLs and repository DOIs in its reference list (the software and data it cites) and its supplementary links (“is-supplemented-by”: its own code or data). A paper that gives its code only in the text, a footnote or a data availability statement is not counted, and some publishers deposit reference lists without their URLs, so every count here is a lower bound. Repository details come from the GitHub REST API (stars, forks, and the repository a fork was made from), the Hugging Face Hub API (likes, downloads, and the base model a model was derived from) and DataCite (Zenodo and other data DOIs, and the GitHub release a Zenodo record archives), read again every month.

## Cite this page

Scholar4AI. (2026). masoudnickparvar/brain-tumor-mri-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-masoudnickparvar-brain-tumor-mri-dataset

```bibtex
@misc{scholar4ai-code-kaggle-masoudnickparvar-brain-tumor-mri-dataset,
  author = {{Scholar4AI}},
  title = {masoudnickparvar/brain-tumor-mri-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-masoudnickparvar-brain-tumor-mri-dataset}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}
```

License: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/).