Deep learning approach to describe and classify fungi microscopic images.
Bartosz ZielińskiAgnieszka Sroka-OleksiakDawid RymarczykAdam PiekarczykMonika Brzychczy-WłochPublished in: PloS one (2020)
Preliminary diagnosis of fungal infections can rely on microscopic examination. However, in many cases, it does not allow unambiguous identification of the species 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 bag-of-words to classify microscopic images of various fungi species. Our approach makes the last stage of biochemical identification redundant, shortening the identification process by 2-3 days, and reducing the cost of the diagnosis.
Keyphrases
- deep learning
- machine learning
- neural network
- bioinformatics analysis
- convolutional neural network
- end stage renal disease
- artificial intelligence
- primary care
- newly diagnosed
- chronic kidney disease
- healthcare
- prognostic factors
- optical coherence tomography
- cardiovascular disease
- patient reported outcomes
- peritoneal dialysis
- big data
- genetic diversity
- cell wall