Enhanced Fetal Plane Classification in Ultrasound Imaging via Prototypical Networks and Few-Shot Learning.

The standard fetal-plane ultrasound images are the most basic in prenatal diagnosis, but the automated classification of these images is difficult due to the paucity of labelled data and the imbalanced class distributions. We present a data-efficient framework that combines a prototypical network with a VGG19 feature extractor (FSL-VGG19) to do few-shot learning, and we compare it to four classical convolutional neural networks (CNNs): MobileNetV2, ResNet50, VGG16, and VGG19 on three publicly available fetal-ultrasound datasets: Maternal-Fetal, FPSU 23, and Africa. Five-fold cross-validation was employed in model selection. FSL-VGG19 attained the accuracies of 96.88%, 97.80%, and 94.38% on the Maternal-Fetal, FPSU 23, and Africa datasets, respectively, outperforming all the classical CNN baselines by 1.1-24.4 percentage points. These rankings of performances were proved to be statistically significant (p < 0.05) by the non-parametric Friedman test, and the Nemenyi post-hoc test was used to confirm that the superiority of FSL-VGG19 was statistically significant relative to the baselines. The sensitivity analysis showed that there was a significant positive relationship between K-shot size and accuracy with a 30.9% difference between one-shot and ten-shot learning in the Maternal-Fetal dataset. Our framework achieved competitive results than recent state-of-the-art approaches with an order of magnitude fewer labelled images. The suggested few-shot architecture minimizes the effects of annotation bottlenecks and the class-imbalance effect, and provides robust and generalizable fetal-plane classification, which is especially applicable to resource-poor clinical environments.