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Updated: Aug 9, 2026
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Published on: June 29, 2013
Improved Birthweight Prediction With Feature-Wise Linear Modulation, GRU, and Attention Mechanism in Ultrasound Data
G Mohana Priya1, S K B Sangeetha1
1Department of Computer Science and Engineering, SRM Institute of Science and Technology, Vadapalani Campus, Chennai, India.
Objectives:
Birthweight prediction in fetal development presents a challenge in direct measurement and often depends on empirical formulas based on the clinician's experience. Existing methods suffer from low accuracy and high execution times, limiting their clinical effectiveness. This study aims to introduce a novel approach integrating feature-wise linear modulation (FiLM), gated recurrent unit (GRU), and Attention network to improve birthweight prediction using ultrasound data.
Methods:
The proposed method utilizes FiLM for adaptive modulation, dynamically adjusting layer activations based on input specifics for enhanced information extraction. GRU is employed to capture sequential dependencies, recognizing the evolving maternal and fetal parameters during pregnancy. The Attention network selectively focuses on crucial parameters, dynamically adjusting feature weights for accurate predictions. The study evaluates classification accuracies for three groups: appropriate-for-gestational-age, large-for-gestational-age, and small-for-gestational-age (SGA). Prediction errors are minimized by optimizing parameters and using mean squared error as the loss function. Experimental evaluations are performed using multiple metrics.
Results:
The proposed strategy attains a high prediction accuracy of 98.8%, outperforming existing methods such as ensemble transfer learning model (83.5%), BabyNet++ (91.7%), bi-directional LSTM with CNN and a hybrid whale with oppositional fruit fly optimization (89.2%), linear regression-random forest-artificial neural network (79.5%), and Attention MFP-Unet (93.6%). The integrated network provides advanced insights into birthweight dynamics, enhancing both interpretability and accuracy.
Conclusions:
The findings of this study are vital for birthweight prediction, clinical delivery guideline development, and implementation of decision-making. The proposed approach supports clinicians in making informed decisions during obstetric examinations and assists pregnant women in weight management, showcasing significant advancements in maternal healthcare.
