---
title: "navoneel/brain-mri-images-for-brain-tumor-detection"
description: "10 papers in the tracked journals link to the Kaggle dataset navoneel/brain-mri-images-for-brain-tumor-detection in their Crossref records, the first in 2021; most of them (2) in 2024."
url: https://scholar4ai.com/code/kaggle-navoneel-brain-mri-images-for-brain-tumor-detection
markdown_url: https://scholar4ai.com/code/kaggle-navoneel-brain-mri-images-for-brain-tumor-detection.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "10 papers in the tracked journals link to the Kaggle dataset navoneel/brain-mri-images-for-brain-tumor-detection in their Crossref records, the first in 2021; most of them (2) in 2024. (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)
---

# navoneel/brain-mri-images-for-brain-tumor-detection: papers linking to it

Research papers in the 480 tracked journals that link to navoneel/brain-mri-images-for-brain-tumor-detection (https://www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection), 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/navoneel/brain-mri-images-for-brain-tumor-detection

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

## Key findings

- 10 papers in the tracked journals link to the Kaggle dataset navoneel/brain-mri-images-for-brain-tumor-detection in their Crossref records, the first in 2021; most of them (2) in 2024.
- By category: AI (9) and Information Systems (1); the journals with the most are Neural Computing and Applications (5), Applied Intelligence (2) and Information Systems Frontiers (1).
- 2 of the papers build on one another: a later paper cites an earlier one that also links navoneel/brain-mri-images-for-brain-tumor-detection.

## By year

*Papers linking to navoneel/brain-mri-images-for-brain-tumor-detection by publication year*

| Year | Papers |
|---|---:|
| 2021 | 2 |
| 2022 | 2 |
| 2023 | 2 |
| 2024 | 2 |
| 2025 | 1 |
| 2026 | 1 |

## Journals

*Journals whose papers link to navoneel/brain-mri-images-for-brain-tumor-detection most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 5 |
| [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | 2 |
| [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | 1 |
| [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | 1 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 1 |

## Family tree

Each paper sits under the earlier paper it builds on.

- [An attention-guided convolutional neural network for automated classification of brain tumor from MRI](https://doi.org/10.1007/s00521-022-07742-z) (Neural Computing and Applications, 2022)
  - [A novel multi-class brain tumor classification method based on unsupervised PCANet features](https://doi.org/10.1007/s00521-023-08281-x) (Neural Computing and Applications, 2023) — cites the paper above

## Papers

*Papers linking to navoneel/brain-mri-images-for-brain-tumor-detection, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2021-07-23 | [A comprehensive survey on feature selection in the various fields of machine learning](https://doi.org/10.1007/s10489-021-02550-9) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Pradip Dhal, Chandrashekhar Azad | 503 | cites it |
| 2022-09-01 | [An attention-guided convolutional neural network for automated classification of brain tumor from MRI](https://doi.org/10.1007/s00521-022-07742-z) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Sumeet Saurav, Ayush Sharma, Ravi Saini et al. | 82 | cites it |
| 2023-01-13 | [Tumor Localization and Classification from MRI of Brain using Deep Convolution Neural Network and Salp Swarm Algorithm](https://doi.org/10.1007/s12559-022-10096-2) | [Cognitive Computation](https://scholar4ai.com/journals/cognitive-computation.md) | Jaber Alyami, Amjad Rehman, Fahad F. Almutairi et al. | 59 | cites it |
| 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 |
| 2022-02-02 | [An improved hybrid classification of brain tumor MRI images based on conglomeration feature extraction techniques](https://doi.org/10.1007/s00521-022-06929-8) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Twinkle Bansal, Neeru Jindal | 26 | cites it |
| 2021-03-07 | [Active contour model with adaptive weighted function for robust image segmentation under biased conditions](https://doi.org/10.1016/j.eswa.2021.114811) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Aditi Joshi, Mohammed Saquib Khan, Asim Niaz et al. | 16 | cites it |
| 2023-02-28 | [A novel multi-class brain tumor classification method based on unsupervised PCANet features](https://doi.org/10.1007/s00521-023-08281-x) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ahmed I. Shahin, Saleh Aly, Walaa Aly | 13 | cites it |
| 2024-01-17 | [Tools for Healthcare Data Lake Infrastructure Benchmarking](https://doi.org/10.1007/s10796-023-10468-5) | [Information Systems Frontiers](https://scholar4ai.com/journals/information-systems-frontiers.md) | Tommaso Dolci, Lorenzo Amata, Carlo Manco et al. | 4 | cites it |
| 2025-07-11 | [Multi-level thresholding segmentation for brain tumor detection using optimized deep learning approach](https://doi.org/10.1007/s00521-025-11398-w) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Ahmed A. Ewees, Fatma Helmy Ismail, N. S. Labeeb et al. | 1 | cites it |
| 2026-09-25 | [Efficient few-shot visual anomaly detection and localization via prompt tuning](https://doi.org/10.1007/s10489-026-07497-3) | [Applied Intelligence](https://scholar4ai.com/journals/applied-intelligence.md) | Jiwoo Choi, Chang Ouk Kim | 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). navoneel/brain-mri-images-for-brain-tumor-detection: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-navoneel-brain-mri-images-for-brain-tumor-detection

```bibtex
@misc{scholar4ai-code-kaggle-navoneel-brain-mri-images-for-brain-tumor-detection,
  author = {{Scholar4AI}},
  title = {navoneel/brain-mri-images-for-brain-tumor-detection: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-navoneel-brain-mri-images-for-brain-tumor-detection}},
  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/).