Machine learning techniques on homological persistence features for prostate cancer diagnosis.

ABSTRACT: The rapid evolution of image processing equipment and techniques ensures the development of novel picture analysis methodologies. One of the most powerful yet computationally possible algebraic techniques for measuring the topological characteristics of functions is persistent homology. It's an algebraic invariant that can capture topological details at different spatial resolutions. Persistent homology investigates the topological features of a space using a set of sampled points, such as pixels. It can track the appearance and disappearance of topological features caused by changes in the nested space created by an operation known as filtration, in which a parameter scale, in our case the intensity of pixels, is increased to detect changes in the studied space over a range of varying sca

SUBMITTER: Rammal A 

PROVIDER: S-EPMC9652917 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature