The DOI matches its Crossref record.
Fractal analysis in the detection of colonic cancer images
Abstract
The aim of this study was to investigate the value of fractal dimension in separating normal and cancerous images, and to examine the relationship between fractal dimension and traditional texture analysis features. Forty-four normal images and 58 cancer images from sections of the colon were analyzed. A "leave-one-out" analysis approach was used to classify the samples into each group. With fractal analysis there was a highly significant difference between groups (p < 0.0001). Correlation and entropy features showed greater differences between the groups (p < 0.0001). Nevertheless, the addition of fractal analysis to the feature analysis improved the sensitivity from 90% to 95% and specificity from 86% to 93%.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Doyle, S., Feldman, M., Tomaszewski, J., & Madabhushi, A. (2012) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2010.2053540 · Cited by 253
Constrained deep weak supervision for histopathology image segmentation
Jia, Z., Huang, X., Chang, E. I.-C., & Xu, Y. (2017) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2017.2724070 · Cited by 242
Acar, E., Plopper, G. E., & Yener, B. (2012) · PLoS ONE · doi:10.1371/journal.pone.0032227 · Cited by 35
Metadata
- Authors
- Esgiar, A. N., Naguib, R. N. G., Sharif, B. S., Bennett, M. K., & Murray, A.
- Venue
- IEEE Transactions on Information Technology in Biomedicine
- Published
- 2002-03
- Volume
- 6
- Issue
- 1
- Pages
- 54-58
- ISSN
- 1089-7771 (ISSN-L)
- OpenAlex
- W2144349513
- Cited by
- 161
- References
- 0
- FWCI
- 3.01
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Fractal analysis in the detection of colonic cancer images. https://app.cochat.ai/papers/10.1109/4233.992163
- The paper
- Esgiar, A. N., Naguib, R. N. G., Sharif, B. S., Bennett, M. K., & Murray, A. (2002). Fractal analysis in the detection of colonic cancer images. IEEE Transactions on Information Technology in Biomedicine. https://doi.org/10.1109/4233.992163
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
