Species recognition of Aspergillus spores using convolutional neural networks in scanning electron microscopy imagery

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Abstract

This paper presents a practical recognition method based on deep learning techniques, for fungal species, through Scanning Electron Microscopy (SEM)
images. A small number of images was acquired. To increase target
signatures and optimize the baseline quality of inputs for object recognition. To tackle the challenge of detecting varied scale targets, a sophisticated and powerful
Convolutional Neural Network (CNN) based on faster region R-CNN, with the prepared training dataset, was trained. In this study, the datasets for five different species of Aspergillus were previously collected via SEM. The proposed method is applied to identify the spore structures – conidia – in the images so as to recognize the species respectively. The initial experimental results show that the
developed method can qualitatively and quantitatively identify the relevant species effectively, being of major importance for the development of easier diagnostic and identification tools in mycology.
Original languageEnglish
Pages (from-to)8-13
Number of pages6
JournalInternational Journal of Pharma Medicine and Biological Sciences
Volume11
Issue number1
DOIs
Publication statusPublished - 1 Jan 2022

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