Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters

Aug 4, 2026

Fetal ultrasound imaging creates diagnostic fetal images from uterine sound waves, enabling measurement of gestational sac, biparietal diameter, head circumference, abdominal circumference, and femur length for clinical review by radiologists and gynecologists. The study reviews machine learning and deep learning approaches for detecting abnormalities in ultrasound images and concentrates on two trimesters with three key fetal parameters: HC, AC, and FL. An improved U-Net segmentation method performs segmentation and predicts parameters, using transfer learning classification models. Experiments use 1,313 fetal images (563 second trimester, 750 third trimester). Reported segmentation accuracies include AC 69.09%, FL 92.02%, HC 69.43% for trimester 2, and FL 90.04%, HC 69.76% for trimester 3; MobileNet and XceptionNet achieve similar high classification performance.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages29

Reading Time1 h 13 min

AccessFree to download

Abstract

Introduction

Prenatal ultrasound and objectives

Trimesters and gestational age estimation

Motivation for pregnancy outcome prediction

Materials and Methods

U-Net segmentation and parameter prediction

Transfer learning classification models

Experiments and Results

Dataset description

Segmentation accuracy across trimesters

Classification performance comparison

Conclusion

Gornale, Shivanand, Kamat, Priyanka, Siddalingappa, Rashmi ORCID logoORCID: [https://orcid.org/0000-0001-9786-8436 and](https://orcid.org/0000-0001-9786-8436 and)[ ](https://orcid.org/0000-0001-9786-8436 and)Kumar, Sathish (2024) Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters. Transactions on Machine Learning and Artificial Intelligence, 12 (3) . pp. 18-45. Downloaded from: [https://ray.yorksj.ac.uk/id/eprint/12837/](https://ray.yorksj.ac.uk/id/eprint/12837/) The version presented here may differ from the published version or version of record. If you intend to cite from the work you are advised to consult the publisher's version: [https://doi.org/10.14738/tecs.123.16985](https://doi.org/10.14738/tecs.123.16985) Research at York St John (RaY) is an institutional repository. It supports the principles of open access by making the research outputs of the University available in digital form. Copyright of the items stored in RaY reside with the authors and/or other copyright owners. Users may access full text items free of charge, and may download a copy for private study or non-commercial research. For further reuse terms, see licence terms governing individual outputs. Institutional Repositories Policy Statement RaY Research at the University of York St John For more information please contact RaY at [ray@yorksj.ac. uk](ray@yorksj.ac. uk) Deep Learning Techniques for a Comprehensive Analysis of Fetal Biometric Parameters Across Trimesters Shivanand Gornale ORCID: 0000-0001-5373-4049 Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India Priyanka Kamat Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India Rashmi Siddalingappa ORCID: 0000-0001-9786-8436 Department of Mechanical and Aerospace Engineering, West Virginia University, Morgantown, West Virginia, USA Sathish Kumar ORCID: 0000-0001-9374-1980 Department of Computer Science, School of Mathematics and Computing Sciences, Rani Channamma University, Belagavi, Karnataka, India ABSTRACT The process of creating fetal images from the uterus using sound influence is known as fetal ultrasound imaging. During this scan, measurements such asthe gestational sac, biparietal diameter, head circumference, abdominal circumference, and femur length can be taken from the mother, which are further analyzed by the radiologist or gynecologist. These factors allow us to quickly test for anomalies and monitor the fetal growth and development of a baby. This paper delves into the techniques utilized in previous studies for analyzing abnormalities from ultrasound images using machine learning and deep learning techniques. Specifically, we focus on two trimesters and three key fetal parameters: Head Circumference (HC), Abdominal Circumference (AC), and Femur Length (FL). Our proposed method, the Unet segmentation method, not only performs segmentation but also predicts the parameters. We employ various transfer learning techniques for classification. The experiment involves 1,313 medical fetal images, comprising 563 from the second trimester and 750 from the third trimester. In terms of segmentation accuracy, the results for the second trimester's biometric parameters are as follows: AC = 69.09%, FL = 92.02%, and HC = 69.43%. For the third trimester, the accuracy for FL is 90.04%, and for HC, it is 69.76%. Regarding classification methods, MobileNet and XceptionNet yield comparable results. For the second trimester, MobileNet achieves 99.28%, and XceptionNet achieves 99.82%. For the third trimester, both MobileNet and XceptionNet achieve 99.86%. Keywords: Anomaly detection, Abdominal circumference, Biparietal diameter, Deep learning techniques, Femur length, Head circumference, Machine Learning, MobileNet, Segmentation accuracy, Transfer learn

FAQs

What problem does the paper address in fetal ultrasound imaging?

It targets the analysis of abnormalities from fetal ultrasound images by extracting and evaluating fetal biometric parameters for prenatal assessment across trimesters.

Which fetal parameters and trimesters are analyzed in the proposed approach?

The method focuses on two trimesters and three parameters: head circumference (HC), abdominal circumference (AC), and femur length (FL).

What models are used for segmentation and classification, and how do they compare?

Segmentation and parameter prediction are performed using a U-Net approach, while classification uses transfer learning with MobileNet and XceptionNet, which show comparable accuracy results across both trimesters.

Aug 4, 2026

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