FetalDenseNet: multi-scale deep learning for enhanced early detection of fetal anatomical planes in prenatal ultrasound.
Authors
Affiliations (6)
Affiliations (6)
- MU Institute for Data Science and Informatics, University of Missouri-Columbia, Columbia, MO, USA.
- School of Science and Technology, 421966 Bangladesh Open University , Gazipur, Bangladesh.
- Department of Computer and Communication Engineering, Patuakhali Science and Technology University, Dumki, Bangladesh.
- Department of Computer Science and Engineering, University of Barishal, Barishal, Bangladesh.
- Department of Physiology, Shaheed Suhrawardy Medical College, Dhaka, Bangladesh.
- Department of Computer Science and Engineering, Military Institute of Science and Technology, Dhaka, Bangladesh.
Abstract
The study aims to improve the classification of fetal anatomical planes using Deep Learning (DL) methods to enhance the accuracy of fetal ultrasound interpretation. Five Convolutional Neural Network (CNN) architectures, such as VGG16, ResNet50, InceptionV3, DenseNet169, and MobileNetV2, are evaluated on a large-scale, clinically validated dataset of 12,400 ultrasound images from 1,792 patients. Preprocessing methods, including scaling, normalization, label encoding, and augmentation, are applied to the dataset, and the dataset is split into 80 % for training and 20 % for testing. Each model was fine-tuned and evaluated based on its classification accuracy for comparison. DenseNet169 achieved the highest classification accuracy of 92 % among all the tested models. The study shows that CNN-based models, particularly DenseNet169, significantly improve diagnostic accuracy in fetal ultrasound interpretation. This advancement reduces error rates and provides support for clinical decision-making in prenatal care.