Automatic classification of fungal-fungal interactions using deep leaning models
Marjan Mansourvar, Jonathan Funk, Søren D. Petersen, Sajad Tavakoli, Jakob Blæsbjerg Hoof, David Llorente Corcoles, Sabrina Pittroff, Lars Jelsbak, Niels Bjerg Jensen, Ling Ding, Rasmus John Normand Frandsen
Abstract
and individual isolates from a collection of 38,400 fungal strains. The authors trained multiple deep learning architectures and evaluated their performance. The results strongly support our approach, achieving a peak accuracy of 95.0 % with the DenseNet121 model and a maximum macro-averaged F1-Score of 93.1 across five folds. To the best of our knowledge, this paper introduces the first automated method for classifying fungal-fungal interactions using deep learning, which can easily be adapted for other fungal species.
Topics & Concepts
Artificial intelligenceComputational biologyComputer scienceBiologyPlant Pathogens and Fungal DiseasesFungal Biology and ApplicationsCell Image Analysis Techniques