Enhancing Fetal Medical Image Analysis through Attention-guided Convolution: A Comparative Study with Established Models
Muna Al‐Razgan, Yasser A. Ali, Emad Mahrous Awwad
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.