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An ensemble deep learning framework for foetal plane identification

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Abstract

Antenatal (prenatal) care stipulates periodic monitoring of the foetus in alleviating risk factors and improving pregnancy outcomes. Foetal images generated from the ultrasound during prenatal care can be classified into different planes, such as the brain, abdomen, femur, etc., and can be further used for the abnormality detection, disease diagnosis, and foetal growth estimation of the foetus. Therefore, recognizing the foetal plane is one of the initial steps to automate antenatal care. This paper proposes an Ensemble Convolution Neural Network (ECNN) model that combines the base models to classify the foetal planes by using an open-access database containing 12,400 images with six foetal planes. Prior studies on this database involved state-of-the-art CNN methods, and Densenet-169 pre-trained model gave an accuracy of 93.6%. Three pre-trained CNN models (base learners) are trained using the transfer learning approach in the proposed method. The predicted features derived from the base learners are then used to train a Deep Neural Network (DNN) based meta-learner to achieve high classification rates. The stacked ensemble model resulted in an accuracy of \(96\%\), which is better than the accuracy of the individual pre-trained models.

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Data availability

The data used in the study was obtained from a open access website: https://zenodo.org/record/3904280.

References

  1. Ekabua J, Ekabua K, Njoku C (2011) Proposed framework for making focused antenatal care services accessible: a review of the Nigerian setting. Int Sch Res Not 2011:253964

    Google Scholar 

  2. He F, Wang Y, Xiu Y, Zhang Y, Chen L (2021) Artificial intelligence in prenatal ultrasound diagnosis. Front Med. https://doi.org/10.3389/fmed.2021.729978

    Article  PubMed  PubMed Central  Google Scholar 

  3. Harikumar S, Akhil AS, Kaimal R (2019) A depth-based nearest neighbor algorithm for high-dimensional data classification. Turk. J. Electr. Eng. Comput. Sci. 27(6):4082–4101

    Article  Google Scholar 

  4. Itoo F, Meenakshi Singh S (2021) Comparison and analysis of logistic regression, Naïve Bayes and KNN machine learning algorithms for credit card fraud detection. Int J Inf Technol (Singap) 13(4):1503–1511

    Article  Google Scholar 

  5. Harikumar S (2020) Blended models for nearest neighbour algorithms for high dimensional smart medical data. Smart medical data sensing and IoT systems design in healthcare. IGI Global, Beijing, pp 48–75

    Chapter  Google Scholar 

  6. Hari Prakash S, Adithya Narayan K, Nair GS, Harikumar S (2022) Perceiving machine learning algorithms to analyze COVID-19 radiographs. In: Proceedings of international conference on recent trends in computing. Springer, pp. 293–305

  7. Bridget ON, Prasad R, Onime C, Ali AA (2021) Drug resistant tuberculosis classification using logistic regression. International Journal of Information Technology (Singapore) 13(2):741–749

    Article  Google Scholar 

  8. Burgos-Artizzu XP, Coronado-Gutiérrez D, Valenzuela-Alcaraz B, Bonet-Carne E, Eixarch E, Crispi F, Gratacós E (2020) Evaluation of deep convolutional neural networks for automatic classification of common maternal fetal ultrasound planes. Sci Rep 10(1):1–12

    Article  Google Scholar 

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

    Article  ADS  CAS  PubMed  Google Scholar 

  10. Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556

  11. Carneiro G, Mateus D, Peter L, Bradley A, Tavares JMR, Belagiannis V, Papa JP, Nascimento JC, Loog M, Lu Z et al (2016) Deep learning and data labeling for medical applications: first international workshop, LABELS 2016, and second international workshop, DLMIA 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings, vol 10008. Springer

  12. Kalaiselvi T, Padmapriya ST, Sriramakrishnan P, Somasundaram K (2020) Deriving tumor detection models using convolutional neural networks from MRI of human brain scans. Int J Inf Technol (Singap) 12(2):403–408

    Article  Google Scholar 

  13. Tan C, Sun F, Kong T, Zhang W, Yang C, Liu C (2018) A survey on deep transfer learning. In: International conference on artificial neural networks. Springer, pp. 270–279

  14. Gopakumar G, Sai Subrahmanyam GR (2019) Deep learning applications to cytopathology: a study on the detection of malaria and on the classification of leukaemia cell-lines. Handbook of deep learning applications. Springer, Cham, pp 219–257

    Chapter  Google Scholar 

  15. Mamun MA, Kabir MS, Akter M, Uddin MS (2022) Recognition of human skin diseases using inception-V3 with transfer learning. Int J Inf Technol (Singap) 14(6):3145–3154

    Article  Google Scholar 

  16. Kora P, Ooi CP, Faust O, Raghavendra U, Gudigar A, Chan WY, Meenakshi K, Swaraja K, Plawiak P, Acharya UR (2021) Transfer learning techniques for medical image analysis: a review. Biocybern Biomed Eng 42:79–107

    Article  Google Scholar 

  17. Wu L, Cheng J-Z, Li S, Lei B, Wang T, Ni D (2017) FUIQA: fetal ultrasound image quality assessment with deep convolutional networks. IEEE Trans Cybern 47(5):1336–1349

    Article  PubMed  Google Scholar 

  18. Walker MC, Willner I, Miguel OX, Murphy MS, El-Chaâr D, Moretti F, Dingwall Harvey AL, Rennicks White R, Muldoon KA, Carrington AM et al (2022) Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester. PLoS One 17(6):0269323

    Article  Google Scholar 

  19. Rueda S, Fathima S, Knight CL, Yaqub M, Papageorghiou AT, Rahmatullah B, Foi A, Maggioni M, Pepe A, Tohka J et al (2013) Evaluation and comparison of current fetal ultrasound image segmentation methods for biometric measurements: a grand challenge. IEEE Trans Med Imaging 33(4):797–813

    Article  PubMed  Google Scholar 

  20. Heuvel TL, Bruijn D, Korte CL, Ginneken BV (2018) Automated measurement of fetal head circumference using 2D ultrasound images. PLoS One 13(8):0200412

    Google Scholar 

  21. Despeisse M, Ford S (2015) The role of additive manufacturing in improving resource efficiency and sustainability. In: IFIP international conference on advances in production management systems, pp 129–136. Springer

  22. Baumgartner CF, Kamnitsas K, Matthew J, Fletcher TP, Smith S, Koch LM, Kainz B, Rueckert D (2017) SonoNet: real-time detection and localisation of fetal standard scan planes in freehand ultrasound. IEEE Trans Med Imaging 36(11):2204–2215

    Article  PubMed  Google Scholar 

  23. Chen H, Ni D, Qin J, Li S, Yang X, Wang T, Heng PA (2015) Standard plane localization in fetal ultrasound via domain transferred deep neural networks. IEEE J Biomed Health Inf 19(5):1627–1636

    Article  Google Scholar 

  24. Liu X, Annangi P, Gupta M, Yu B, Padfield D, Banerjee J, Krishnan K (2012) Learning-based scan plane identification from fetal head ultrasound images. In: Bosch JG, Doyley MM (eds) Medical imaging 2012: ultrasonic imaging, tomography, and therapy. society of photo-optical instrumentation engineers (SPIE) conference series, vol 8320. p 83200

  25. Qu R, Xu G, Ding C, Jia W, Sun M (2020) Standard plane identification in fetal brain ultrasound scans using a differential convolutional neural network. IEEE Access 8:83821–83830

    Article  Google Scholar 

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

    Article  Google Scholar 

  27. Sridar P, Kumar A, Quinton AE, 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  PubMed  Google Scholar 

  28. Wang X, Liu Z, Du Y, Diao Y, Liu P, Lv G, Zhang H (2021) Recognition of fetal facial ultrasound standard plane based on texture feature fusion. Comput Math Methods Med 2021:1–12

    Article  ADS  Google Scholar 

  29. Sarwar A, Ali M, Manhas J, Sharma V (2020) Diagnosis of diabetes type-ii using hybrid machine learning based ensemble model. Int J Inf Technol (Singap) 12(2):419–428

    Article  Google Scholar 

  30. Ju C, Bibaut A, Laan M (2018) The relative performance of ensemble methods with deep convolutional neural networks for image classification. J Appl Stat 45(15):2800–2818

    Article  MathSciNet  PubMed  Google Scholar 

  31. Aloysius N, Geetha M (2021) An ensembled scale-space model of deep convolutional neural networks for sign language recognition. Advances in artificial intelligence and data engineering. Springer, Singapore, pp 363–375

    Chapter  Google Scholar 

  32. Zheng Y, Li C, Zhou X, Chen H, Xu H, Li Y, Zhang H, Li X, Sun H, Huang X et al (2022) Application of transfer learning and ensemble learning in image-level classification for breast histopathology. arXiv preprint arXiv:2204.08311

  33. Xue D, Zhou X, Li C, Yao Y, Rahaman MM, Zhang J, Chen H, Zhang J, Qi S, Sun H (2020) An application of transfer learning and ensemble learning techniques for cervical histopathology image classification. IEEE Access 8:104603–104618

    Article  Google Scholar 

  34. Shorfuzzaman M (2022) An explainable stacked ensemble of deep learning models for improved melanoma skin cancer detection. Multimedia Syst 28(4):1309–1323

    Article  Google Scholar 

  35. Wolpert DH (1992) Stacked generalization. Neural Netw 5(2):241–259

    Article  Google Scholar 

  36. He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 770–778

  37. Huang G, Liu Z, Maaten L, Weinberger KQ (2017) Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amritapuri, Kollam, 690525, Kerala, India

    Seena Thomas & Sandhya Harikumar

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  1. Seena Thomas
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  2. Sandhya Harikumar
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Correspondence to Seena Thomas.

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Thomas, S., Harikumar, S. An ensemble deep learning framework for foetal plane identification. Int. j. inf. tecnol. 16, 1377–1386 (2024). https://doi.org/10.1007/s41870-023-01709-6

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