andrewmvd/lung-and-colon-cancer-histopathological-images: papers linking to it

Kaggle · www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to andrewmvd/lung-and-colon-cancer-histopathological-images (https://www.kaggle.com/datasets/andrewmvd/lung-and-colon-cancer-histopathological-images), 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 andrewmvd/lung-and-colon-cancer-histopathological-images in their Crossref records, the first in 2024; most of them (2) in 2024.
By category: AI (3); the journals with the most are Neural Computing and Applications (2) and Expert Systems with Applications (1).

By year#

20242025

Papers linking to andrewmvd/lung-and-colon-cancer-histopathological-images by publication year
Year	Papers
2024	2
2025	1

Journals#

Journals whose papers link to andrewmvd/lung-and-colon-cancer-histopathological-images most
Journal	Papers
Neural Computing and Applications	2
Expert Systems with Applications	1

Papers linking to andrewmvd/lung-and-colon-cancer-histopathological-images, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-11-15	Colon cancer classification and detection by novel CMNV2 model and methods of deep learning	Neural Computing and Applications	B. Anil Kumar, Neeraj Kumar Misra	27	cites it
2024-05-13	ELCD-NSC2: a novel early lung cancer detection and non-small cell classification framework	Neural Computing and Applications	Hadeer A. Helaly, Mahmoud Badawy, Eman M. El-Gendy et al.	4	cites it
2025-12-18	DBNA-Net: A deep learning framework for the classification of lung and colon cancer using histopathological images	Expert Systems with Applications	Ganeshan Ramasamy, Sreenu Ponnada, Balashanmuga Vadivu Palanivel et al.	3	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). andrewmvd/lung-and-colon-cancer-histopathological-images: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-andrewmvd-lung-and-colon-cancer-histopathological-images

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

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