基于深度学习算法的超声医学图像分类研究进展

Fairoz Q. Kareem, A. Abdulazeez
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引用次数: 14

摘要

随着医疗领域技术和智能设备的发展,计算机系统已经成为医疗领域学习设备的重要组成部分。其中一种学习方法是深度学习(DL),它是机器学习(ML)的一个分支。深度学习方法被用于该领域,因为它是通过其算法获得准确结果的现代方法之一,其中在该领域使用的算法有卷积神经网络(CNN)和递归神经网络(RNN)。本文综述了国内外研究人员在解决胎儿问题方面所做的工作,并对超声图像分割分类在不同任务中的应用进行了总结和细致的讨论。最后,本研究讨论了将深度学习应用于超声图像分析的潜在挑战和方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Ultrasound Medical Images Classification Based on Deep Learning Algorithms: A Review
With the development of technology and smart devices in the medical field, the computer system has become an essential part of this development to learn devices in the medical field. One of the learning methods is deep learning (DL), which is a branch of machine learning (ML). The deep learning approach has been used in this field because it is one of the modern methods of obtaining accurate results through its algorithms, and among these algorithms that are used in this field are convolutional neural networks (CNN) and recurrent neural networks (RNN). In this paper we reviewed what have researchers have done in their researches to solve fetal problems, then summarize and carefully discuss the applications in different tasks identified for segmentation and classification of ultrasound images. Finally, this study discussed the potential challenges and directions for applying deep learning in ultrasound image analysis.
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