A Comparative Study of Different Segmentation and Regression Models for Fetal Head Circumference Measurement

Constant fetal health monitoring during pregnancy becomes crucial in assessing and diagnosing natural fetal growth. Out of all the methods, the ultrasound imaging-based approach for fetal aspect measurement is quite commonly used in modern days. An extensive comparative approach of various regressio...

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Bibliographic Details
Published in:	2024 Second International Conference on Emerging Trends in Information Technology and Engineering (ICETITE) pp. 1 - 6
Main Authors:	Ahmed, Pathik, Yusuf, Md. Salah Uddin, Sarker, Sowmik, Chowdhury, Abdullahi, Bhowmik, Angkita, Rahman, Faria
Format:	Conference Proceeding
Language:	English
Published:	IEEE 22-02-2024
Subjects:	Analytical models Comparative Study Deep Learning Fetal Head Circumference Head Image analysis Image segmentation Medical Imaging Regression Models Segmentation Mod- els Time measurement Ultrasonic imaging Ultrasonic variables measurement Ultrasound Image Analysis
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Summary:	Constant fetal health monitoring during pregnancy becomes crucial in assessing and diagnosing natural fetal growth. Out of all the methods, the ultrasound imaging-based approach for fetal aspect measurement is quite commonly used in modern days. An extensive comparative approach of various regression and segmentation models has been taken in this paper in the measurement of fetal head circumference. This research focuses on optimal model selection out of CNN regression models and segmentation models. Out-of-regression CNN models ResNet-50, InceptionV3, EfficientNet, and out-of-segmentation models U- net and Attention-Unet have been considered and implemented. Utilizing evaluation matrices such as MAE, RMSE, accuracy, and standard deviation for comparative analysis, the model designs were examined. This research work was carried out on the public dataset named HC-18 containing 805 multi-scaled ultrasound images. With room for improvement in this research work, it is highly evident that Inception V3 performed the best having an accuracy of about 87.04% whereas U-net performed the worst, having an accuracy of about 73.85%.
DOI:	10.1109/ic-ETITE58242.2024.10493596