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Comparative Study of Principle and Independent Component Analysis of CNN for Embryo Stage and Fertility Classification

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  • Anurag Sinha

    (Amity University, Ranchi, India)

  • Tannisha Kundu

    (Amity University, Ranchi, India)

  • Kshitiz Sinha

    (Shandong University, China)

Abstract

background: Applications of deep learning for the societal issues are one of the debatable concerns where the community medicine and implication of artificial intelligence for the societal issues are a big concern. This article, it is shown the applications of neural networks in clinical practice for reproduction procedure enhancement. And this is a well-known issue where image analysis has the exact applications. In Embryology, fetal abnormality early-stage detection and diagnosis is one of the challenging tasks and thus, needs automation in the process of tomography and ultrasonic imaging. Also, Interpretation and accuracy in the medical imaging process are very important for accurate results.

Suggested Citation

  • Anurag Sinha & Tannisha Kundu & Kshitiz Sinha, 2022. "Comparative Study of Principle and Independent Component Analysis of CNN for Embryo Stage and Fertility Classification," International Journal of Fuzzy System Applications (IJFSA), IGI Global, vol. 11(2), pages 1-28, April.
    • Handle: RePEc:igg:jfsa00:v:11:y:2022:i:2:p:1-28
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