Deep learning approach to description and classification of fungi microscopic images
This addresses a critical issue for immunosuppressed patients by speeding up diagnosis, though it is incremental as it builds on existing deep learning methods.
The paper tackles the problem of ambiguous fungal species identification from microscopic images, which delays targeted treatment, by applying a deep learning and bag-of-words approach to classify these images, reducing the identification process by 2-3 days and cutting diagnostic costs.
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 treatment is grave in consequences as the mortality rate for immunosuppressed patients is high. In this paper, we apply machine learning approach based on deep learning 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 diagnostic examination.