Abstract
Early prognosis of fungal contagium may be based on minute examination using microscope. In most cases, however, it becomes unfit for the abstract identification of species because of their apparent similarity. So, it becomes absolute necessary to employ more biochemical tests. In order to detect and identify the nine fungal species from the microscopic images, transfer learning has been deployed by the authors without data augmentation and got 95.45% classification accuracy. Data augmentation has also been applied on the dataset under consideration then fed into the network and got 94.77% classification accuracy. Inception V3 network was used for the study.
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Sharma, A., Lakhnotra, A., Manhas, J., Padha, D. (2022). Deep Learning Based Classification of Microscopic Fungal Images. In: Rathore, V.S., Sharma, S.C., Tavares, J.M.R., Moreira, C., Surendiran, B. (eds) Rising Threats in Expert Applications and Solutions. Lecture Notes in Networks and Systems, vol 434. Springer, Singapore. https://doi.org/10.1007/978-981-19-1122-4_21
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DOI: https://doi.org/10.1007/978-981-19-1122-4_21
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