Medical Images Analysis in Cancer Diagnostic
Jelena Vasiljevi\'c, Ivica Milosavljevi\'c, Vladimir Krsti\'c,, Nata\v{s}a Zivi\'c, Lazar Berbakov, Luka Lopu\v{s}ina, Dhinaharan Nagamalai, and Milutin Cerovi\'c
TL;DR
This paper explores the use of multifractal analysis of medical images to improve cancer diagnosis by distinguishing malignant tissue from normal and benign tissue, aiming to reduce subjective errors.
Contribution
It introduces a novel application of multifractal analysis to assist in cancer diagnosis, enhancing accuracy and reducing subjective judgment in medical image classification.
Findings
Multifractal parameters correlate with malignant cell characteristics.
Application of multifractal analysis improves diagnostic accuracy.
Reduces subjective errors in cancer detection.
Abstract
This paper shows results of computer analysis of images in the purpose of finding differences between medical images in order of their classifications in terms of separation malign tissue from a normal and benign tissue. The diagnostics of malign tissue is of the crucial importance in medicine. Therefore, ascertainment of the correlation between multifractals parameters and "chaotic" cells could be of the great appliance. This paper shows the application of multifractal analysis for additional help in cancer diagnosis, as well as diminishing. of the subjective factor and error probability
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Taxonomy
TopicsDigital Imaging for Blood Diseases · Genetics, Bioinformatics, and Biomedical Research · Cell Image Analysis Techniques