EfficientNetB0 with Geometric & Color augmentation and TrivialAugmentWide achieves the best performance for rabies classification on a small imbalanced set of 155 fluorescent images via 3-fold cross-validation.
Development of a fuores- cent antibody virus neutralisation test (F A VN test) for the quantitation of rabies-neutralising antibody
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Rabies diagnosis in low-data settings: A comparative study on the impact of data augmentation and transfer learning
EfficientNetB0 with Geometric & Color augmentation and TrivialAugmentWide achieves the best performance for rabies classification on a small imbalanced set of 155 fluorescent images via 3-fold cross-validation.