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Original Article

Smart Pregnancy Care System: Real-time Fetal Brain Abnormality Detection and Maternal Health using IoT

Open Access Peer Reviewed Crossref DOI

Authors

  • Harish A G1
  • Dr. ManojKumar S B2
  • Jeevangowda K M3
  • Nishchitha S R4
  • Rachana B5

12345Department of ECE, BGS Institute of Technology, Adhichunchanagiri University, Karnataka, India

Article Information

Published Online
May-June 2025
Pages
50-56
Article Type
Original Article
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References

1. Smith,J,.et al. "A Comprehensive Review on Fetal Brain Abnormality Detection Techniques in Ultrasound Imaging", 2017.
2. Patel,A. et,al "Real-time Fetal Brain Abnormality Detection Using YOLOv3 in 4D Ultrasound Imaging", 2018.
3. Wang, L., et al. "Segmentation of Fetal Brain Structures in MRI Using a Region-Based Convolutional Neural Network", 2019.
4. Garcia, M., et al. "Hybrid Approach for Fetal Brain Abnormality Detection: A Fusion of Deep Learning and Genetic Algorithms", 2020.
5. Kumar,S., et al. “Hybrid approaches in enhancing the robustness of fetal brain abnormality detection systems”,2021.
6. “Use of MRI in the diagnosis of fetal brain abnormalities in utero (MERIDIAN): a multicentre, prospective cohort study. - PubMed -
NCBI.” [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/27988140. [Accessed: 25-Aug-2018].
7. M. Havaei et al., “Brain tumor segmentation with Deep Neural Networks,” Med. Image Anal., vol. 35, pp. 18–31, Jan. 2017.
8. A. Alansary et al., “Automatic Brain Localization in Fetal MRI Using Superpixel Graphs,” in Machine Learning Meets Medical
Imaging, 2015, pp. 13–22.
9. K. E. al et, “Fetal brain anomalies detection during the first trimester: expanding the scope of antenatal sonography. - PubMed
- NCBI.” [Online]. Available: https://www.ncbi.nlm.nih.gov/pubmed/28282781. [Accessed: 25-Aug-2018].
10. B. J. Erickson, P. Korfiatis, Z. Akkus, and T. L. Kline, “Machine Learning for Medical Imaging,” Radiographics, vol. 37, no. 2, pp.
505–515, Mar. 2017 [6].
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