awsaf49/cbis-ddsm-breast-cancer-image-dataset: papers linking to it

Kaggle · www.kaggle.com/datasets/awsaf49/cbis-ddsm-breast-cancer-image-dataset

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to awsaf49/cbis-ddsm-breast-cancer-image-dataset (https://www.kaggle.com/datasets/awsaf49/cbis-ddsm-breast-cancer-image-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.

Key findings#

3 papers in the tracked journals link to the Kaggle dataset awsaf49/cbis-ddsm-breast-cancer-image-dataset in their Crossref records, the first in 2025; most of them (3) in 2025.
By category: AI (3); the journals with the most are Expert Systems with Applications (2) and Neural Computing and Applications (1).

By year#

20252025

Papers linking to awsaf49/cbis-ddsm-breast-cancer-image-dataset by publication year
Year	Papers
2025	3

Journals#

Journals whose papers link to awsaf49/cbis-ddsm-breast-cancer-image-dataset most
Journal	Papers
Expert Systems with Applications	2
Neural Computing and Applications	1

Papers linking to awsaf49/cbis-ddsm-breast-cancer-image-dataset, most cited first
Published	Title	Journal	Authors	Citations	Link
2025-06-16	Enhancing mammogram classification using explainable Conditional Self-Attention Generative Adversarial Network	Expert Systems with Applications	K. Sreekala, Jayakrushna Sahoo	8	cites it
2025-09-04	Breast cancer classification by converging tumour region probability density and texture feature-based clustering	Neural Computing and Applications	Bersha Kumari, Amita Nandal, Arvind Dhaka et al.	1	cites it
2025-10-28	Student–teacher model based breast cancer classification approach with depthwise separable layers	Expert Systems with Applications	Yogesh Haridas Jadhav, Bhuvan Unhelkar, Pravin R. Kshirsagar 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). awsaf49/cbis-ddsm-breast-cancer-image-dataset: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-awsaf49-cbis-ddsm-breast-cancer-image-dataset

@misc{scholar4ai-code-kaggle-awsaf49-cbis-ddsm-breast-cancer-image-dataset,
  author = {{Scholar4AI}},
  title = {awsaf49/cbis-ddsm-breast-cancer-image-dataset: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-awsaf49-cbis-ddsm-breast-cancer-image-dataset}},
  urldate = {2026-11-03},
  note = {Journal and Conference Research Rankings; data from OpenAlex (CC0); licensed CC BY-NC 4.0}
}

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