Fractal analysis in the detection of colonic cancer images
Year: 2002
Type: article
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%.
DOI: 10.1109/4233.992163
Authors: A.N. Esgiar, R.N.G. Naguib, B.S. Sharif, M.K. Bennett, A. Murray
Institutions: Newcastle University, Coventry University, Freeman Hospital
Language: English
Topic: AI in cancer detection
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2144349513