Rabies diagnosis in low-data settings: A comparative study on the impact of data augmentation and transfer learning
This study addresses the challenges of rabies diagnosis in low-resource regions such as Africa and Asia, where reliance on fluorescence microscopy and expert interpretation is hindered by scarce samples and a shortage of trained personnel. The authors propose a deep learning–based automated diagnostic approach and systematically evaluate multiple transfer learning architectures—including EfficientNetB0, EfficientNetB2, VGG16, and Vision Transformer (ViT)—alongside various data augmentation strategies on a small dataset of only 155 fluorescence images. They demonstrate for the first time that TrivialAugmentWide effectively preserves critical fluorescent features while enhancing model generalization. Among the tested configurations, EfficientNetB0 combined with tailored augmentation achieves optimal performance on cropped images. The results indicate that reliable and rapid automated diagnosis is attainable even under extreme data scarcity and class imbalance, and the method has already been deployed as an online tool for real-world use.