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- Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy
- Haozhong Ma , Jinshan Yang , Xiaolu Chen , Xinyu Jiang , Yimin Su , Shanlei Qiao , Guowei Zhong
- J. Microbiol. 2021;59(6):563-572. Published online March 29, 2021
- DOI: https://doi.org/10.1007/s12275-021-1013-z
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- 11 Web of Science
- 12 Crossref
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Abstract
-
Fungi of the genus Aspergillus are ubiquitously distributed
in nature, and some cause invasive aspergillosis (IA) infections
in immunosuppressed individuals and contamination
in agricultural products. Because microscopic observation
and molecular detection of Aspergillus species represent the
most operator-dependent and time-intensive activities, automated
and cost-effective approaches are needed. To address
this challenge, a deep convolutional neural network (CNN)
was used to investigate the ability to classify various Aspergillus
species. Using a dissecting microscopy (DM)/stereomicroscopy
platform, colonies on plates were scanned with
a 35× objective, generating images of sufficient resolution for
classification. A total of 8,995 original colony images from
seven Aspergillus species cultured in enrichment medium
were gathered and autocut to generate 17,142 image crops
as training and test datasets containing the typical representative
morphology of conidiophores or colonies of each strain.
Encouragingly, the Xception model exhibited a classification
accuracy of 99.8% on the training image set. After training,
our CNN model achieved a classification accuracy of
99.7% on the test image set. Based on the Xception performance
during training and testing, this classification algorithm
was further applied to recognize and validate a new
set of raw images of these strains, showing a detection accuracy
of 98.2%. Thus, our study demonstrated a novel concept
for an artificial-intelligence-based and cost-effective detection
method
ology for Aspergillus organisms, which also has the potential to improve the public’s understanding of the fungal kingdom. -
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