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Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis

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Abstract

In the realm of maternal healthcare, accurate fetal plane detection is of paramount importance. This paper introduces a novel approach that leverages ensemble techniques to enhance the precision and dependability of fetal plane classification. We address two pivotal classification tasks: first, the categorization of fetal planes into six distinct classes, encompassing critical regions such as the fetal abdomen, brain, femur, thorax, maternal cervix, and other areas. The second task focuses on the nuanced classification of fetal brain planes, further refined into trans-thalamic, trans-cerebellum, and trans-ventricular subtypes. To address these challenges, we harnessed the power of six distinct pre-trained models, rigorously training each for 50 epochs. Our results underscore the consistent superiority of InceptionResNetV2, DenseNet121, and Xception. Through a pioneering ensemble approach, we synergistically harnessed their capabilities, leading to enhanced classification performance. This research promises to augment the precision of maternal-fetal plane classification, with the potential to revolutionize clinical practice and healthcare research.

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Data Availability

The first author can provide the data supporting the study’s conclusions upon reasonable request.

References

  1. Hadlock FP, Harrist R, Sharman RS, Deter RL, Park SK (1985) Estimation of fetal weight with the use of head, body, and femur measurements-a prospective study. Am J Obstet Gynecol 151(3):333–337

    Article  Google Scholar 

  2. Miller J, Turan S, Baschat AA (2008) Fetal growth restriction. In: Seminars in perinatology, vol 32. No. 4. WB Saunders

  3. Nicolaides KH, Syngelaki A, Ashoor G, Birdir C, Touzet G (2012) Noninvasive prenatal testing for fetal trisomies in a routinely screened first-trimester population. Am J Obstet Gynecol 207(5):374–1

    Article  Google Scholar 

  4. Figueras F, Gratacós E (2014) Update on the diagnosis and classification of fetal growth restriction and proposal of a stage-based management protocol. Fetal Diagn Ther 36(2):86–98

    Article  Google Scholar 

  5. Burgos-Artizzu XP, Perez-Moreno Á, Coronado-Gutierrez D, Gratacos E, Palacio M (2019) Evaluation of an improved tool for non-invasive prediction of neonatal respiratory morbidity based on fully automated fetal lung ultrasound analysis. Sci Rep 9(1):1950

    Article  Google Scholar 

  6. Kagan K, Sonek J (2015) How to measure cervical length. Ultrasound Obstet Gynecol 45(3):358–362

    Article  Google Scholar 

  7. Baños N, Perez-Moreno A, Julià C, Murillo-Bravo C, Coronado D, Gratacos E, Deprest J, Palacio M (2018) Quantitative analysis of cervical texture by ultrasound in mid-pregnancy and association with spontaneous preterm birth. Ultrasound Obstet Gynecol 51(5):637–643

    Article  Google Scholar 

  8. Kwitt R, Vasconcelos N, Razzaque S, Aylward S (2013) Localizing target structures in ultrasound video-a phantom study. Med Image Anal 17(7):712–722

    Article  Google Scholar 

  9. Rahmatullah B, Papageorghiou AT, Noble JA (2012) Integration of local and global features for anatomical object detection in ultrasound. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2012: 15th International Conference, Nice, France, October 1-5, 2012, Proceedings, Part III 15, pp 402–409, Springer, Berlin

  10. Salomon L, Winer N, Bernard J, Ville Y (2008) A score-based method for quality control of fetal images at routine second-trimester ultrasound examination. Prenat Diagn 28(9):822–827

    Article  Google Scholar 

  11. Chaitanya TVSS, Kumar RP, Chaudhary D, Singh S, Shrivastav P, Yadav D (2023) Numerical solution of first and second order differential equations with deep neural networks. In: 2023 IEEE World Conference on Applied Intelligence and Computing (AIC), pp 180–186

  12. Hoyert Donna L & Gregory E. C. (2022) Cause-of-death Data From the Fetal Feath File, 2018–2020

  13. LeCun Y, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444

    Article  Google Scholar 

  14. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, Thrun S (2017) Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639):115–118

    Article  Google Scholar 

  15. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, Van Der Laak JA, Van Ginneken B, Sánchez CI (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88

    Article  Google Scholar 

  16. Mehta N, Prasad S (2021) Speckle noise reduction and entropy minimization approach for medical images. Int J Inf Technol 13:1457–1462

    Google Scholar 

  17. Paul P, Shan BP (2023) Preprocessing techniques with medical ultrasound common carotid artery images. Soft Computing:1–21

  18. Chucherd S, Makhanov SS (2011) Sparse phase portrait analysis for preprocessing and segmentation of ultrasound images of breast cancer. IAENG International Journal of Computer Science, 38.2

  19. Lu Y, Fu X, Chen F, Wong KK (2020) Prediction of fetal weight at varying gestational age in the absence of ultrasound examination using ensemble learning. Artif Intell Med 102:101748

    Article  Google Scholar 

  20. Zhang B, Liu H, Luo H, Li K (2021) Automatic quality assessment for 2d fetal sonographic standard plane based on multitask learning. Medicine 100.4

  21. Cai Y, Sharma H, Chatelain P, Noble JA (2018) Sonoeyenet: standardized fetal ultrasound plane detection informed by eye tracking. In: 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), pp 1475–1478

  22. Ryou H, Yaqub M, Cavallaro A, Roseman F, Papageorghiou A, Noble JA (2016) Automated 3d ultrasound biometry planes extraction for first trimester fetal assessment. In: Machine learning in medical imaging: 7th International Workshop, MLMI 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, 17 Oct 2016, Proceedings 7, pp 196–204, Springer

  23. Cai Y, Sharma H, Chatelain P, Noble JA (2018) Multi-task sonoeyenet: detection of fetal standardized planes assisted by generated sonographer attention maps. In: Medical Image Computing and Computer Assisted Intervention–MICCAI 2018: 21st International Conference, Granada, Spain, September 16-20, 2018, Proceedings, Part I, pp 871–879, Springer

  24. Selvathi D, Chandralekha R (2022) Fetal biometric based abnormality detection during prenatal development using deep learning techniques. Multidimens Syst Signal Process 33:1–15

    Article  Google Scholar 

  25. Fiorentino MC, Villani FP, Di Cosmo M, Frontoni E, Moccia S (2023) A review on deep-learning algorithms for fetal ultrasound-image analysis. Med Image Anal 83:102629

    Article  Google Scholar 

  26. Krishna TB, Kokil P (2023) Automated classification of common maternal fetal ultrasound planes using multi-layer perceptron with deep feature integration. Biomed Signal Process Control 86:105283

    Article  Google Scholar 

  27. Krishna TB, Kokil P (2024) Standard fetal ultrasound plane classification based on stacked ensemble of deep learning models. Expert Syst Appl 238:122153

    Article  Google Scholar 

  28. Prabakaran BS, Hamelmann P, Ostrowski E, Shafique M (2023) Fpus23: an ultrasound fetus phantom dataset with deep neural network evaluations for fetus orientations, fetal planes, and anatomical features. IEEE Access

  29. Dan T, Chen X, He M, Guo H, He X, Chen J, **an J, Hu Y, Zhang B, Wang N (2023) Deepga for automatically estimating fetal gestational age through ultrasound imaging. Artif Intell Med 135:102453

    Article  Google Scholar 

  30. Bronsgeest K, Lust EE, Henneman L, Crombag N, Bilardo CM, Stemkens D, Galjaard R-JH, Sikkel E, Hout SH, Bekker MN (2023) Current practice of first-trimester ultrasound screening for structural fetal anomalies in developed countries. Prenat Diagn 43(7):873–880

    Article  Google Scholar 

  31. Krishna TB, Kokil P (2022) Automated detection of common maternal fetal ultrasound planes using deep feature fusion. In: 2022 IEEE 19th India Council International Conference (INDICON), pp 1–5

  32. Pu B, Li K, Li S, Zhu N (2021) Automatic fetal ultrasound standard plane recognition based on deep learning and iiot. IEEE Trans Industr Inf 17(11):7771–7780

    Article  Google Scholar 

  33. Sobhaninia Z, Rafiei S, Emami A, Karimi N, Najarian K, Samavi S, Soroushmehr SR (2019) Fetal ultrasound image segmentation for measuring biometric parameters using multi-task deep learning. In: 2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp 6545–6548

  34. Sridar P, Kumar A, Quinton A, Nanan R, Kim J, Krishnakumar R (2019) Decision fusion-based fetal ultrasound image plane classification using convolutional neural networks. Ultrasound Med Biol 45(5):1259–1273

    Article  Google Scholar 

  35. Agarwal S, Tarui T, Patel V, Turner A, Nagaraj U, Venkatesan C (2023) Prenatal neurologic diagnosis: challenges in neuroimaging, prognostic counseling, and prediction of neurodevelopmental outcomes. Pediatr Neurol 142:6067

    Article  Google Scholar 

  36. Nemec S, Schwarz-Nemec U, Prayer D, Weber M, Bettelheim D, Kasprian G (2023) Femur development in fetal growth restriction as observed on prenatal magnetic resonance imaging. Ultrasound Obstet Gynecol 61(5):601–609

    Article  Google Scholar 

  37. Whitby E, Gaunt T (2023) Fetal lung MRI and features predicting post-natal outcome: a sco** review of the current literature. Br J Radiol 96:20220344

    Article  Google Scholar 

  38. Cetin O, Karaman E, Alisik M, Erel O, Kolusari A, Sahin HG (2022) The evaluation of maternal systemic thiol/disulphide homeostasis for the short-term prediction of preterm birth in women with threatened preterm labour: a pilot study. J Obstet Gynaecol 42(6):1972–1977

    Article  Google Scholar 

  39. Kaur H, Rani J (2016) Mri brain image enhancement using histogram equalization techniques. In: 2016 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET), pp 770–773

  40. Thepade SD, Pardhi PM (2022) Contrast enhancement with brightness preservation of low light images using a blending of clahe and bpdhe histogram equalization methods. Int J Inf Technol 14(6):3047–3056

    Google Scholar 

  41. Rahman S, Rahman MM, Abdullah-Al-Wadud M, Al-Quaderi GD, Shoyaib M (2016) An adaptive gamma correction for image enhancement. EURASIP J Image Video Process 2016(1):1–13

    Article  Google Scholar 

  42. Deng G, Cahill L (1993) An adaptive gaussian filter for noise reduction and edge detection. In: 1993 IEEE Conference Record Nuclear Science Symposium and Medical Imaging Conference, pp 1615–1619

  43. Incze A, Jancsó H-B, Szilágyi Z, Farkas A, Sulyok C (2018) Bird sound recognition using a convolutional neural network. In: 2018 IEEE 16th International Symposium on Intelligent Systems and Informatics (SISY), pp 000295–000300

  44. Ahmed S, Islam S (2023) Robust median filtering forensics using texture feature and deep fully connected network. Int J Inf Technol 16:1–10

    Google Scholar 

  45. Liu N, Zhai G (2017) Free energy adjusted peak signal to noise ratio (fea-psnr) for image quality assessment. Sens Imaging 18:1–10

    Article  Google Scholar 

  46. Annamalai R, Sudharson S, Sindhu KG (2023) Facial matching and reconstruction techniques in identification of missing person using deep learning. In: 2023 IEEE 4th Annual Flagship India Council International Subsections Conference (INDISCON), pp 1–7

  47. Szegedy C, Ioffe S, Vanhoucke V, Alemi A (2017) Inception-v4, inception-resnet and the impact of residual connections on learning. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol 31

  48. Talukder MSH, Akter S (2023) An improved ensemble model of hyper parameter tuned ml algorithms for fetal health prediction. International Journal of Information Technology:1–10

  49. Chen H, Dou Q, Ni D, Cheng J-Z, Qin J, Li S, Heng P-A (2015) Automatic fetal ultrasound standard plane detection using knowledge transferred recurrent neural networks. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part I 18, pp 507–514, Springer

  50. Krishna TB, Kokil P (2022) Automated detection of common maternal fetal ultrasound planes using deep feature fusion. In: 2022 IEEE 19th India Council International Conference (INDICON), pp 1–5

  51. Thomas S, Harikumar S (2024) An ensemble deep learning framework for foetal plane identification. International Journal of Information Technology:1–10

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, Tamil Nadu, India

    Annamalai R & Kolla Gnapika Sindhu

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  1. Annamalai R
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Correspondence to Kolla Gnapika Sindhu.

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R, A., Sindhu, K.G. Ensemble-based advancements in maternal fetal plane and brain plane classification for enhanced prenatal diagnosis. Int. j. inf. tecnol. (2024). https://doi.org/10.1007/s41870-024-01806-0

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