Deep learning applied to analyze patterns from evaporated droplets of Viscum album extracts.

ABSTRACT: This paper introduces a deep learning based methodology for analyzing the self-assembled, fractal-like structures formed in evaporated droplets. To this end, an extensive image database of such structures of the plant extract Viscum album Quercus [Formula: see text] was used, prepared by three different mixing procedures (turbulent, laminar, and diffusion based). The proposed pattern analysis approach is based on two stages: (1) automatic selection of patches that exhibit rich texture along the database; and (2) clustering of patches in accordance with prevalent texture by means of a Dense Convolutional Neural Network. The fractality of the patterns in each cluster is verified through Local Connected Fractal Dimension histograms. Experiments with Gray-Level Co-Occurrence matrices are perfo

SUBMITTER: Acuna C 

PROVIDER: S-EPMC9468023 | biostudies-literature | 2022 Sep

REPOSITORIES: biostudies-literature