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

Anurag Sinha, Tannisha Kundu, Kshitiz Sinha
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引用次数: 1

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.
CNN原理与独立分量分析在胚胎分期与育性分类中的比较研究
背景:深度学习在社会问题上的应用是一个有争议的问题,其中社区医学和人工智能对社会问题的影响是一个大问题。本文介绍了神经网络在生殖过程增强方面的临床应用。这是一个众所周知的问题图像分析有确切的应用。在胚胎学中,胎儿异常的早期检测和诊断是一项具有挑战性的任务,因此需要在断层扫描和超声成像过程中实现自动化。此外,医学成像过程中的解释和准确性对于准确的结果非常重要。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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