Fetal-Net: enhancing Maternal-Fetal ultrasound interpretation through Multi-Scale convolutional neural networks and Transformers.
Authors
Affiliations (8)
Affiliations (8)
- Department of Computer Science, IQRA National University, Swat Campus, KPK, Peshawar, Pakistan.
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia.
- King Salman Center for Disability Research, Riyadh, Saudi Arabia.
- Department of Software Engineering, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 1543, Saudi Arabia.
- School of Computing, Ulster University, Belfast, UK.
- King Abdullah the II IT School, The University of Jordan, Amman, 11942, Jordan. [email protected].
- Head of Database at Ministry of Public Health, Riyadh, Afghanistan.
- Head of Database at Ministry of Public Health, Riyadh, Afghanistan. [email protected].
Abstract
Ultrasound imaging plays an important role in fetal growth and maternal-fetal health evaluation, but due to the complicated anatomy of the fetus and image quality fluctuation, its interpretation is quite challenging. Although deep learning include Convolution Neural Networks (CNNs) have been promising, they have largely been limited to one task or the other, such as the segmentation or detection of fetal structures, thus lacking an integrated solution that accounts for the intricate interplay between anatomical structures. To overcome these limitations, Fetal-Net-a new deep learning architecture that integrates Multi-Scale-CNNs and transformer layers-was developed. The model was trained on a large, expertly annotated set of more than 12,000 ultrasound images across different anatomical planes for effective identification of fetal structures and anomaly detection. Fetal-Net achieved excellent performance in anomaly detection, with precision (96.5%), accuracy (97.5%), and recall (97.8%) showed robustness factor against various imaging settings, making it a potent means of augmenting prenatal care through refined ultrasound image interpretation.