| Title | Deep convolutional neural network: a novel approach for the detection of Aspergillus fungi via stereomicroscopy |
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| Author | Haozhong Ma, Jinshan Yang, Xiaolu Chen, Xinyu Jiang, Yimin Su, Shanlei Qiao*, and Guowei Zhong* |
| Address | Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing 211166, P. R. China |
| Bibliography | Journal of Microbiology, 59(6),563–572, 2021, |
| DOI | 10.1007/s12275-021-1013-z |
| Key Words | Aspergillus detection, conidiophore and colony morphology, stereomicroscopy/dissecting microscopy, convolutional neural network (CNN), Xception |
| 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 methodology for Aspergillus organisms, which also has the potential to improve the public’s understanding of the fungal kingdom. |