Classification of Microorganisms from Sparsely Limited Data Using a Proposed Deep Learning Ensemble
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-99-1624-5_22
Reference20 articles.
1. Li Z, Li C, Yao Y, Zhang J, Rahaman MM, Xu H et al (2021) Environmental microorganism image dataset fifth version for multiple image analysis tasks. PLoS ONE 16(5):99–110. https://doi.org/10.10007/1234567890
2. Waquar (2022) Micro-organism image classification. Kaggle. https://doi.org/10.34740/KAGGLE/DSV/4032122
3. Poomrittigul S, Chomkwah W, Tanpatanan T, Sakorntanant S, Treebupachatsakul T (2022) A comparison of deep learning CNN architecture models for classifying bacteria. In: 2022 37th International technical conference on circuits/systems, computers and communications (ITC-CSCC), pp 290–293. https://doi.org/10.1109/ITC-CSCC55581.2022.9894986
4. Chen W, Liu P, Lai C, Lin Y (2022) Identification of environmental microorganism using optimally fine-tuned convolutional neural network. Environ Res 206:112610. ISSN 0013-9351, https://doi.org/10.1016/j.envres.2021.112610
5. Kim HE, Maros ME, Siegel F, Ganslandt T (2022) Rapid convolutional neural networks for gram-stained image classification at inference time on mobile devices: empirical study from transfer learning to optimization. Biomedicines 10:2808. https://doi.org/10.3390/biomedicines10112808
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