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Original Article
Smart Pregnancy Care System: Real-time Fetal Brain Abnormality Detection and Maternal Health using IoT
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
Published Online: May-June 2025
Pages: 50-56
Cite this article
↗ https://www.doi.org/10.59256/ijrtmr.20250503004References
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].
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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