---
title: "ahmedhamada0/brain-tumor-detection: papers citing it"
description: "15 papers in the tracked journals link to the Kaggle dataset ahmedhamada0/brain-tumor-detection in their Crossref records, the first in 2022; most of them (7) in 2025."
url: https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection
markdown_url: https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection.md
license: CC BY-NC 4.0 (https://creativecommons.org/licenses/by-nc/4.0/)
share_text: "15 papers in the tracked journals link to the Kaggle dataset ahmedhamada0/brain-tumor-detection in their Crossref records, the first in 2022; 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)
---

# ahmedhamada0/brain-tumor-detection: papers linking to it

Research papers in the 480 tracked journals that link to ahmedhamada0/brain-tumor-detection (https://www.kaggle.com/datasets/ahmedhamada0/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/ahmedhamada0/brain-tumor-detection

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

## Key findings

- 15 papers in the tracked journals link to the Kaggle dataset ahmedhamada0/brain-tumor-detection in their Crossref records, the first in 2022; most of them (7) in 2025.
- By category: AI (15); the journals with the most are Neural Computing and Applications (7), Expert Systems with Applications (3) and Knowledge-Based Systems (1).
- 4 of the papers build on one another: a later paper cites an earlier one that also links ahmedhamada0/brain-tumor-detection.

## By year

*Papers linking to ahmedhamada0/brain-tumor-detection by publication year*

| Year | Papers |
|---|---:|
| 2022 | 2 |
| 2023 | 2 |
| 2024 | 2 |
| 2025 | 7 |
| 2026 | 2 |

## Journals

*Journals whose papers link to ahmedhamada0/brain-tumor-detection most*

| Journal | Papers |
|---|---:|
| [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | 7 |
| [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | 3 |
| [Knowledge-Based Systems](https://scholar4ai.com/journals/knowledge-based-systems.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.

- [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
  - [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, 2025) — cites the paper above
    - [HayabusaNet: Hybrid attention-based multiscale fusion CNN for accurate and efficient brain tumor classification in MRI scans](https://doi.org/10.1016/j.eswa.2026.133135) (Expert Systems with Applications, 2026) — cites the paper above

## Papers

*Papers linking to ahmedhamada0/brain-tumor-detection, most cited first*

| Published | Title | Journal | Authors | Citations | Link |
|---|---|---|---|---:|---|
| 2023-10-18 | [Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification](https://doi.org/10.1016/j.eswa.2023.122159) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Muhammed ÇELİK, Özkan İni̇k | 153 | cites it |
| 2024-11-20 | [Explainable ensemble deep learning-based model for brain tumor detection and classification](https://doi.org/10.1007/s00521-024-10401-0) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Khalid Mohamed Hosny, Mahmoud Mohammed, Rania A. Salama et al. | 85 | 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 |
| 2025-03-24 | [A secure hybrid deep learning framework for brain tumor detection and classification](https://doi.org/10.1186/s40537-025-01117-6) | [Journal Of Big Data](https://scholar4ai.com/journals/journal-of-big-data.md) | Sandeep Kumar Mathivanan, Saravanan Srinivasan, Manjula Sanjay Koti et al. | 47 | 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 |
| 2022-12-15 | [A variate brain tumor segmentation, optimization, and recognition framework](https://doi.org/10.1007/s10462-022-10337-8) | [Artificial Intelligence Review](https://scholar4ai.com/journals/artificial-intelligence-review.md) | Hossam Magdy Balaha, Asmaa El-Sayed Hassan | 33 | 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 |
| 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 |
| 2025-10-24 | [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 |
| 2025-01-02 | [DeepCancer: deep learning for brain tumor detection-based application system](https://doi.org/10.1007/s00521-024-10926-4) | [Neural Computing and Applications](https://scholar4ai.com/journals/neural-computing-and-applications.md) | Suleyman Al-Showarah | 7 | cites it |
| 2026-02-02 | [DyC-CLIP: Dynamic context-aware multi-modal prompt learning for zero-shot anomaly detection](https://doi.org/10.1016/j.patcog.2026.113215) | [Pattern Recognition](https://scholar4ai.com/journals/pattern-recognition.md) | Peng Chen, Fangjun Huang, Chao Huang et al. | 6 | 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 |
| 2026-06-05 | [HayabusaNet: Hybrid attention-based multiscale fusion CNN for accurate and efficient brain tumor classification in MRI scans](https://doi.org/10.1016/j.eswa.2026.133135) | [Expert Systems with Applications](https://scholar4ai.com/journals/expert-systems-with-applications.md) | Rizal Dwi Prayogo, Siti Amatullah Karimah, Hidetaka Nambo | 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 |
| 2025-10-15 | [Towards building robust models for unimodal and multimodal medical imaging data](https://doi.org/10.1016/j.inffus.2025.103822) | [Information Fusion](https://scholar4ai.com/journals/information-fusion.md) | Joy Dhar, Puneet Goyal, Maryam Haghighat et al. | 1 | 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). ahmedhamada0/brain-tumor-detection: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-ahmedhamada0-brain-tumor-detection

```bibtex
@misc{scholar4ai-code-kaggle-ahmedhamada0-brain-tumor-detection,
  author = {{Scholar4AI}},
  title = {ahmedhamada0/brain-tumor-detection: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-ahmedhamada0-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/).