A New Method in Improving the Accuracy of Fetal Brain Health Diagnosis Based on Image Analysis of Constrictors Feature in Ultrasound Images

Mehran Emadi ¹ ( Assistant Professor, Department of Electrical Engineering, Mobarakeh Branch, Islamic Azad University, Isfahan, Iran )
Samira Surani ² ( Master Student, Ragheb Institute of Higher Education, Isfahan, Iran )

Submited date : 2025-01-09 Accepted date : 2025-05-10

Keywords: Improvement, ِِDiagnosis, Fetal Brain Health, Ultrasound Images, Scattered Transform, Discrete Wavelet Transform.,

Abstract :

Fetal growth is a critical stage in prenatal care that requires timely identification of abnormalities in ultrasound images to protect the health of the fetus and mother. Ultrasound-based imaging has played an essential role in the diagnosis of fetal malformations and abnormalities, however, despite significant advances in ultrasound technology, accurate detection of abnormalities in prenatal images still poses significant challenges, often due to time and It requires considerable expertise from medical professionals. Methods based on machine learning are among the methods that help experts. In this research, a machine learning method based on image scattering features is presented for the classification of fetal brain ultrasound images. In the proposed method, after integration based on the maximum integration rule in scattered features based on discrete wavelet transform and discrete cosine transform, a method based on mutual information is presented for feature selection. The selected features have been used to diagnose the health status of the fetus with the help of the nearest neighbor. The aim is to perform a classification of several K classes of random forest support vector machine (SVM) and used for evaluation.

References:

[1] Z. Alfirevic, G. M. Gyte, A. Cuthbert, and D. Devane, “Continuous cardiotocography (CTG) as a form of electronic fetal monitoring (EFM) for fetal assessment during labour,” Cochrane database of systematic reviews, no. 2, 2017.
[2] N. Muhammad Hussain, A. U. Rehman, M. T. B. Othman, J. Zafar, H. Zafar, and H. Hamam, “Accessing artificial intelligence for fetus health status using hybrid deep learning algorithm (AlexNet-SVM) on cardiotocographic data,” Sensors, vol. 22, no. 14, pp. 5103, 2022.
[3] K. Barnova, R. Martinek, R. Vilimkova Kahankova, R. Jaros, V. Snasel, and S. Mirjalili, “Artificial Intelligence and Machine Learning in Electronic Fetal Monitoring,” Archives of Computational Methods in Engineering, pp. 1-32, 2024.
[4] M. Vafaeezadeh, H. Behnam, and P. Gifani, “Ultrasound Image Analysis with Vision Transformers–Review,” 2024.
[5] M. C. Fiorentino, F. P. Villani, M. Di Cosmo, E. Frontoni, and S. Moccia, “A review on deep-learning algorithms for fetal ultrasound-image analysis,” Medical image analysis, vol. 83, pp. 102629, 2023.
[6] P. H. Diniz, Y. Yin, and S. Collins, “Deep learning strategies for ultrasound in pregnancy,” European Medical Journal. Reproductive health, vol. 6, no. 1, pp. 73, 2020.
[7] P.-H. Yeung, L. S. Hesse, M. Aliasi, M. C. Haak, W. Xie, A. I. Namburete, and I.-s. Consortium, “Sensorless volumetric reconstruction of fetal brain freehand ultrasound scans with deep implicit representation,” Medical Image Analysis, vol. 94, pp. 103147, 2024.
[8] S. Molina Giraldo, N. Torres Valencia, C. Torres Valencia, and H. F. Restrepo, “Anatomical structure characterization of fetal ultrasound images using texture‐based segmentation technique via an interactive MATLAB application,” Journal of Clinical Ultrasound, vol. 52, no. 2, pp. 189-200, 2024.
[9] H. P. Kim, S. M. Lee, J.-Y. Kwon, Y. Park, K. C. Kim, and J. K. Seo, “Automatic evaluation of fetal head biometry from ultrasound images using machine learning,” Physiological measurement, vol. 40, no. 6, pp. 065009, 2019.
[10] J. Yu, Y. Wang, P. Chen, and Y. Shen, “Fetal abdominal contour extraction and measurement in ultrasound images,” Ultrasound in medicine & biology, vol. 34, no. 2, pp. 169-182, 2008.
[11] Z. Cömert, A. F. Kocamaz, and V. Subha, “Prognostic model based on image-based time-frequency features and genetic algorithm for fetal hypoxia assessment,” Computers in biology and medicine, vol. 99, pp. 85-97, 2018.
[12] S. Thomas, and S. Harikumar, “An ensemble deep learning framework for foetal plane identification,” International Journal of Information Technology, pp. 1-10, 2024.
[13] M. Alzubaidi, U. Shah, M. Agus, and M. Househ, “FetSAM: Advanced Segmentation Techniques for Fetal Head Biometrics in Ultrasound Imagery,” IEEE Open Journal of Engineering in Medicine and Biology, 2024.
[14] M. Lin, Q. Zhou, T. Lei, N. Shang, Q. Zheng, X. He, N. Wang, and H. Xie, “Deep learning system improved detection efficacy of fetal intracranial malformations in a randomized controlled trial,” NPJ Digital Medicine, vol. 6, no. 1, pp. 191, 2023.
[15] V. Nagabotu, and A. Namburu, “Precise segmentation of fetal head in ultrasound images using improved U‐Net model,” ETRI Journal, 2023.
[16] H. Ghabri, M. S. Alqahtani, S. Ben Othman, A. Al-Rasheed, M. Abbas, H. A. Almubarak, H. Sakli, and M. N. Abdelkarim, “Transfer learning for accurate fetal organ classification from ultrasound images: a potential tool for maternal healthcare providers,” Scientific Reports, vol. 13, no. 1, pp. 17904, 2023.
[17] M. C. Walker, I. Willner, O. X. Miguel, M. S. Murphy, D. El-Chaâr, F. Moretti, A. L. Dingwall Harvey, R. Rennicks White, K. A. Muldoon, and A. M. Carrington, “Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester,” Plos one, vol. 17, no. 6, pp. e0269323, 2022.
[18] Y. Zhou, “Prediction and value of ultrasound image in diagnosis of fetal central nervous system malformation under deep learning algorithm,” Scientific Programming, vol. 2021, pp. 1-7, 2021.
[19] P. Deepika, R. Suresh, and P. Pabitha, “Defending Against Child Death: Deep learning‐based diagnosis method for abnormal identification of fetus ultrasound Images,” Computational Intelligence, vol. 37, no. 1, pp. 128-154, 2021.
[20] X. P. Burgos-Artizzu, D. Coronado-Gutiérrez, B. Valenzuela-Alcaraz, E. Bonet-Carne, E. Eixarch, F. Crispi, and E. Gratacós, “Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes,” Scientific Reports, vol. 10, no. 1, pp. 10200, 2020.
[21] R. Qu, G. Xu, C. Ding, W. Jia, and M. Sun, “Deep learning-based methodology for recognition of fetal brain standard scan planes in 2D ultrasound images,” Ieee Access, vol. 8, pp. 44443-44451, 2019.
[22] P. Durka, M. Dovgialo, A. Duszyk-Bogorodzka, and P. Biegański, “Two-Stage Atomic Decomposition of Multichannel EEG and the Previously Undetectable Sleep Spindles,” Sensors, vol. 24, no. 3, pp. 842, 2024.
[23] B. A. Olshausen, and D. J. Field, “Sparse coding with an overcomplete basis set: A strategy employed by V1?,” Vision research, vol. 37, no. 23, pp. 3311-3325, 1997.
[24] M. Aharon, M. Elad, and A. Bruckstein, “K-SVD: An algorithm for designing overcomplete dictionaries for sparse representation,” IEEE Transactions on signal processing, vol. 54, no. 11, pp. 4311-4322, 2006.
[25] J. Stromberg, “A Modified Haar System and Higher Order Spline Systems; Conf. in Honor of A. Zygmund,” Wadsworth Math. Series, pp. 475-493, 1981.
[26] Y. Meyer, “Principe d'incertitude, bases hilbertiennes et algebres d'operateurs,” Séminaire Bourbaki,vol. 662, 1986.
[27] I. Daubechies, “Orthonormal bases of compactly supported wavelets,” Communications on pure and applied mathematics, vol. 41, no. 7, pp. 909-996, 1988.
[28] G. Beylkin, “On the representation of operators in bases of compactly supported wavelets,” SIAM Journal on Numerical Analysis, vol. 29, no. 6, pp. 1716-1740, 1992.
[29] A. Benkessirat, and N. Benblidia, "Fundamentals of feature selection: An overview and comparison." pp. 1-6.
[30] M. A. Valizade Hasanloei, R. Sheikhpour, M. A. Sarram, E. Sheikhpour, and H. Sharifi, “A combined Fisher and Laplacian score for feature selection in QSAR based drug design using compounds with known and unknown activities,” Journal of Computer-Aided Molecular Design, vol. 32, no. 2, pp. 375-384, 2018.
[31] M. Azhar, and P. A. Thomas, "Comparative review of feature selection and classification modeling." pp. 1-9.
[32] S. Qi, X. Wu, W.-H. Chen, J. Liu, J. Zhang, and J. Wang, “sEMG-based recognition of composite motion with convolutional neural network,” Sensors and Actuators A: Physical, vol. 311, pp. 112046, 2020.
[33] V. Gude, and S. Corns, “Integrated deep learning and supervised machine learning model for predictive fetal monitoring,” Diagnostics, vol. 12, no. 11, pp. 2843, 2022.
[34] R. Sreelakshmy, A. Titus, N. Sasirekha, E. Logashanmugam, R. B. Begam, G. Ramkumar, and R. Raju, “[Retracted] An Automated Deep Learning Model for the Cerebellum Segmentation from Fetal Brain Images,” BioMed Research International, vol. 2022, no. 1, pp. 8342767, 2022.
[35] Y. Kazemi, and S. Houghten, "A deep learning pipeline to classify different stages of Alzheimer's disease from fMRI data." pp. 1-8.
[36] H. Xie, N. Wang, M. He, L. Zhang, H. Cai, J. Xian, M. Lin, J. Zheng, and Y. Yang, “Using deep‐learning algorithms to classify fetal brain ultrasound images as normal or abnormal,” Ultrasound in Obstetrics & Gynecology, vol. 56, no. 4, pp. 579-587, 2020.
[37] M. Lin, X. He, H. Guo, M. He, L. Zhang, J. Xian, T. Lei, Q. Xu, J. Zheng, and J. Feng, “Use of real‐time artificial intelligence in detection of abnormal image patterns in standard sonographic reference planes in screening for fetal intracranial malformations,” Ultrasound in Obstetrics & Gynecology, vol. 59, no. 3, pp. 304-316, 2022.
[38] L. H. Lee, E. Bradburn, R. Craik, M. Yaqub, S. A. Norris, L. C. Ismail, E. O. Ohuma, F. C. Barros, A. Lambert, and M. Carvalho, “Machine learning for accurate estimation of fetal gestational age based on ultrasound images,” NPJ digital medicine, vol. 6, no. 1, pp. 36, 2023.
[39] W. Li, X. Lin, and X. Chen, “Detecting Alzheimer's disease Based on 4D fMRI: An exploration under deep learning framework,” Neurocomputing, vol. 388, pp. 280-287, 2020.
[40] J. Villar, R. B. Gunier, C. O. Tshivuila-Matala, S. A. Rauch, F. Nosten, R. Ochieng, M. C. Restrepo-Méndez, R. McGready, F. C. Barros, and M. Fernandes, “Fetal cranial growth trajectories are associated with growth and neurodevelopment at 2 years of age: INTERBIO-21st Fetal Study,” Nature medicine, vol. 27, no. 4, pp. 647-652, 2021.