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Species Recognition of Aspergillus Conidia Using Convolutional Neural Networks in Scanning Electron Microscopy Imagery

Huaizhong Zhang1 and Marta Filipa Simões2,3
1.Dept. Computer Science, Edge Hill University, Ormskirk, United Kingdom
2.State Key Laboratory of Lunar and Planetary Sciences, Macau University of Science and Technology, Taipa, Macau SAR, China.
3.Macau Center for Space Exploration and Science, China National Space Administration (CNSA), Macau SAR, China.
AbstractThis 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 circumvent the issue of not having many samples, a method of generating the training set is proposed 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 speciesrespectively. The initial experimental results show that thedeveloped method can qualitatively and quantitativelyidentify the relevant species effectively, being of majorimportance for the development of easier diagnostic andidentification tools in mycology.
Index TermsAspergillus conidia, convolutional neural network, object recognition, scanning electron microscopy

Cite: Song-Huaizhong Zhang and Marta Filipa Simões, "Species Recognition of Aspergillus Conidia Using Convolutional Neural Networks in Scanning Electron Microscopy Imagery," International Journal of Pharma Medicine and Biological Sciences, Vol. 11, No. 1, pp. 8-13, January 2022. doi: 10.18178/ijpmbs.11.1.8-13

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