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

Kaggle · www.kaggle.com/datasets/navoneel/brain-mri-images-for-brain-tumor-detection

← Code and data repositories cited in research papers

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.

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#

20212026

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	5
Applied Intelligence	2
Information Systems Frontiers	1
Cognitive Computation	1
Expert Systems with Applications	1

Family tree#

Each paper sits under the earlier paper it builds on; papers that cite no other paper here start a branch of their own.

An attention-guided convolutional neural network for automated classification of brain tumor from MRI Neural Computing and Applications, 2022
- A novel multi-class brain tumor classification method based on unsupervised PCANet features Neural Computing and Applications, 2023 — cites the paper above

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	Applied Intelligence	Pradip Dhal, Chandrashekhar Azad	503	cites it
2022-09-01	An attention-guided convolutional neural network for automated classification of brain tumor from MRI	Neural Computing and Applications	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	Cognitive Computation	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	Neural Computing and Applications	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	Neural Computing and Applications	Twinkle Bansal, Neeru Jindal	26	cites it
2021-03-07	Active contour model with adaptive weighted function for robust image segmentation under biased conditions	Expert Systems with Applications	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	Neural Computing and Applications	Ahmed I. Shahin, Saleh Aly, Walaa Aly	13	cites it
2024-01-17	Tools for Healthcare Data Lake Infrastructure Benchmarking	Information Systems Frontiers	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	Neural Computing and Applications	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	Applied Intelligence	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

@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}
}

Free to reuse for non-commercial purposes with attribution (CC BY-NC 4.0).