Enhancing Fetal Medical Image Analysis through Attention-guided Convolution: A Comparative Study with Established Models

Abstract

The ability to detect and track fetal growth is greatly aided by medical image analysis, which plays a crucial role in parental care. This study introduces an attention-guided convolutional neural network (AG-CNN) for maternal–fetal ultrasound image analysis, comparing its performance with that of established models (DenseNet 169, ResNet50, and VGG16). AG-CNN, featuring attention mechanisms, demonstrates superior results with a training accuracy of 0.95 and a testing accuracy of 0.94. Comparative analysis reveals AG-CNN’s outperformance against alternative models, with testing accuracies for DenseNet 169 at 0.90, ResNet50 at 0.88, and VGG16 at 0.86. These findings underscore the effectiveness of AG-CNN in fetal image analysis, emphasising the role of attention mechanisms in enhancing model performance. The study’s results contribute to advancing the field of obstetric ultrasound imaging by introducing a novel model with improved accuracy, demonstrating its potential for enhancing diagnostic capabilities in maternal–fetal healthcare.