Deep learning approach to describe and classify fungi microscopic images

• URL: http://arxiv.org/abs/2005.11772v1
• Date: Sun, 24 May 2020 15:15:07 GMT
• Title: Deep learning approach to describe and classify fungi microscopic images
• Authors: Bartosz Zieli\'nski and Agnieszka Sroka-Oleksiak and Dawid Rymarczyk and Adam Piekarczyk and Monika Brzychczy-W{\l}och
• Abstract summary: We apply a machine learning approach based on deep neural networks and Fisher Vector to classify microscopic images of various fungi species. Our approach has the potential to make the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.
• Score: 4.759323753598067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species by microbiologist due to their visual similarity. Therefore, it is usually necessary to use additional biochemical tests. That involves additional costs and extends the identification process up to 10 days. Such a delay in the implementation of targeted therapy may be grave in consequence as the mortality rate for immunosuppressed patients is high. In this paper, we apply a machine learning approach based on deep neural networks and Fisher Vector (advanced bag-of-words method) to classify microscopic images of various fungi species. Our approach has the potential to make the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.

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