nodoubttome/skin-cancer9-classesisic: papers linking to it

Kaggle · www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic

← Code and data repositories cited in research papers

Research papers in the 480 tracked journals that link to nodoubttome/skin-cancer9-classesisic (https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic), 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 nodoubttome/skin-cancer9-classesisic in their Crossref records, the first in 2024; most of them (1) in 2026.
By category: AI (3); the journals with the most are Knowledge-Based Systems (2) and Neural Computing and Applications (1).

By year#

20242026

Papers linking to nodoubttome/skin-cancer9-classesisic by publication year
Year	Papers
2024	1
2025	1
2026	1

Journals#

Journals whose papers link to nodoubttome/skin-cancer9-classesisic most
Journal	Papers
Knowledge-Based Systems	2
Neural Computing and Applications	1

Papers linking to nodoubttome/skin-cancer9-classesisic, most cited first
Published	Title	Journal	Authors	Citations	Link
2024-08-01	SkinNet-14: a deep learning framework for accurate skin cancer classification using low-resolution dermoscopy images with optimized training time	Neural Computing and Applications	Abdullah Al Mahmud, Sami Azam, Inam Ullah Khan et al.	63	cites it
2025-02-21	Optimized Mixed-Order Relation-Aware Recurrent Neural Networks based CAD Model for Skin Cancer Detection and Classification	Knowledge-Based Systems	G.S. Uthayakumar, Mallikarjun Yaramadhi, T. Marimuthu et al.	12	cites it
2026-02-04	Federated vision transformer with adaptive focal loss for medical image classification	Knowledge-Based Systems	Xinyuan Zhao, Yihang Wu, Ahmad Chaddad et al.	2	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). nodoubttome/skin-cancer9-classesisic: papers linking to it. Journal and Conference Research Rankings. Retrieved November 3, 2026, from https://scholar4ai.com/code/kaggle-nodoubttome-skin-cancer9-classesisic

@misc{scholar4ai-code-kaggle-nodoubttome-skin-cancer9-classesisic,
  author = {{Scholar4AI}},
  title = {nodoubttome/skin-cancer9-classesisic: papers linking to it},
  year = {2026},
  howpublished = {\url{https://scholar4ai.com/code/kaggle-nodoubttome-skin-cancer9-classesisic}},
  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).