使用卷积神经网络对医学图像进行分类:一个案例研究

Maad M. Mijwil, Anmar Alkhazraji, Abdel-Hameed W. Al-Mistarehi, R. Doshi, Enas Sh. Mahmood
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引用次数: 0

摘要

卷积神经网络是深度学习架构之一,在很多文献中都有涉及,它在工作中是令人难以置信的。卷积神经网络以其在计算机视觉和图形分析应用中的应用而闻名。它的特点是一个或多个隐藏层的现实性,提取图像或视频中的特征,还有一个层来显示效果。因此,作者决定使用卷积神经网络算法对几张COVID-19患者的胸部x线图像进行分类,并研究该算法的行为以及在训练时将获得的效果。最后,本文的研究表明,该算法的性能和实践是非常优秀的,并且在一个完美的训练时间内取得了令人满意的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Employing a Convolutional Neural Network to Classify Medical Images: A Case Study
A convolutional neural network is one of the deep learning architectures that has been involved in a lot of the literature, and it's incredible at work. The convolutional neural network is distinguished in its use in computer vision and graphical analysis applications. It is characterised by the actuality of one or more hidden layers that extract features in images or videos, and there is also a layer to show the effects. In this regard, the authors decided to involve the convolutional neural network algorithm to classify a few chest X-ray images of COVID-19 patients and study the behaviour of this algorithm and the effects that will be obtained at the time of training. Finally, this study concluded that the performance and practices of this algorithm are very excellent and give satisfactory effects with a perfect training time.
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