[en] Background
Deep neural networks have been successfully applied to diverse fields of computer vision. However, they only outperform human capacities in a few cases.
Methods
The ability of deep neural networks versus human experts to classify microscopy images was tested on biofilm colonization patterns formed on sulfide minerals composed of up to three different bioleaching bacterial species attached to chalcopyrite sample particles.
Results
A low number of microscopy images per category (<600) was sufficient for highly efficient computational analysis of the biofilm's bacterial composition. The use of deep neural networks reached an accuracy of classification of ∼90% compared to ∼50% for human experts.
Conclusions
Deep neural networks outperform human experts’ capacity in characterizing bacterial biofilm composition involved in the degradation of chalcopyrite. This approach provides an alternative to standard, time-consuming biochemical methods.
Disciplines :
Biotechnology Environmental sciences & ecology
Author, co-author :
Buetti-Dinh, Antoine; Università della Svizzera italiana - USI > Institute of Computational Science, Faculty of Informatics ; Swiss Institute of Bioinformatics - SIB
Galli, Vanni; University of Applied Sciences of Southern Switzerland > Institute for Information Systems and Networking
Bellenberg, Sören; Universität Duisburg-Essen > Fakultät für Chemie, Biofilm Centre
Ilie, Olga; Università della Svizzera italiana - USI > Institute of Computational Science, Faculty of Informatics ; Swiss Institute of Bioinformatics - SIB
Herold, Malte ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Christel, Stephan
Boretska, Mariia
Pivkin, Igor V.
Wilmes, Paul ; University of Luxembourg > Luxembourg Centre for Systems Biomedicine (LCSB)
Sand, Wolfgang
Vera, Mario
Dopson, Mark
External co-authors :
yes
Language :
English
Title :
Deep neural networks outperform human expert's capacity in characterizing bioleaching bacterial biofilm composition
