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Fetal Cardiac Detection Using Deep Learning from Echocardiographic Image–A Survey

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IOT with Smart Systems ( ICTIS 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 720))

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Abstract

Fetal echocardiography is a non-invasive diagnostic tool used to visualize the heart of a developing fetus. Machine learning algorithms can be used to aid in the analysis of these images and help detect any potential cardiac anomalies. This involves the use of supervised learning techniques, where the algorithm is trained on a large dataset of labeled images to learn the features and patterns associated with normal and abnormal heart structures. During the testing phase, the algorithm is then able to apply this knowledge to new images to make predictions about the presence of any cardiac anomalies. This technology can help improve the accuracy and speed of fetal echocardiography diagnoses and contribute to better outcomes for expectant mothers and their unborn children. In this paper we can survey the multiple machine learning and deep learning algorithms to improve the efficiency in disease prediction. The CNN can learn to identify features of fetal cardiac tumors and distinguish them from normal cardiac structures. This approach has the potential to improve the accuracy and efficiency of fetal cardiac tumor detection. To analyze the imaging and clinical features of fetal cardiac tumors, and to explore the relationship between tuberous sclerosis complex (TSC) and cardiac rhabdomyoma in the fetus.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, M. Kumarasamy College of Engineering, Thalavapalayam, Karur, Tamilnadu, 639113, India

    D. Pradeep, S. Deva Prasath, J. Jerome Edwin & P. Kumaravel

Authors
  1. D. Pradeep
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  2. S. Deva Prasath
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  3. J. Jerome Edwin
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  4. P. Kumaravel
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Corresponding author

Correspondence to D. Pradeep .

Editor information

Editors and Affiliations

  1. Hertfordshire Business School, University of Hertfordshire, Hatfield, Hertfordshire, UK

    Jyoti Choudrie

  2. Department of AI and DS, Vishwakarma Institute of Information Technology, Pune, India

    Parikshit N. Mahalle

  3. Department of Computer Science, Faculty of Computer Science and Information Technology, University Putra Malaysia, Serdang, Selangor, Malaysia

    Thinagaran Perumal

  4. Global Knowledge Research Foundation, Ahmedabad, Gujarat, India

    Amit Joshi

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© 2023 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

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Pradeep, D., Prasath, S.D., Edwin, J.J., Kumaravel, P. (2023). Fetal Cardiac Detection Using Deep Learning from Echocardiographic Image–A Survey. In: Choudrie, J., Mahalle, P.N., Perumal, T., Joshi, A. (eds) IOT with Smart Systems. ICTIS 2023. Lecture Notes in Networks and Systems, vol 720. Springer, Singapore. https://doi.org/10.1007/978-981-99-3761-5_6

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