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首页> 外文期刊>Advances in Science, Technology and Engineering Systems >Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images
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Convolutional Neural Network Based Classification of Patients with Pneumonia using X-ray Lung Images

机译：基于X射线图像的肺炎肺炎患者卷积神经网络分类

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摘要

Analysis and classification of lung diseases using X-ray images is a primary step in the procedure of pneumonia diagnosis, especially in a critical period as pandemic of COVID-19 that is type of pneumonia. Therefore, an automatic method with high accuracy of classification is needed to perform classification of lung diseases due to the increasing number of cases. Convolutional Neural Networks (CNN) based classification has gained a big popularity over the last few years because of its speed and level of accuracy on the image’s classification tasks. Through this article, we propose an implementation a CNN-based classification models using transfer learning technique to perform pneumonia detection and compare the results in order to detect the best model for the task according to certain parameters. As this has become a fast expanding field, there are several models but we will focus on the best outperforming algorithms according to their architecture, length and type of layers and evaluation parameters for the classification tasks. Firstly, we review the existing conventional methods and deep learning architectures used for segmentation in general. Next, we perform a deep performance and analysis based on accuracy and loss function of implemented models. A critical analysis of the results is made to highlight all important issues to improve.
机译：使用X射线图像的肺病分析和分类是肺炎诊断程序的主要步骤，尤其是作为肺炎的Covid-19大流行的关键时期。因此，由于越来越多的病例，需要具有高精度的自动方法来进行肺病分类。基于卷积神经网络（CNN）的分类在过去几年中获得了很大的普及，因为它的速度和图像的分类任务的准确性水平。通过本文，我们提出了一种实现基于CNN的分类模型，使用传输学习技术进行肺炎检测并比较结果，以便根据某些参数检测任务的最佳模型。由于这已成为一个快速的扩展字段，因此有几种型号，但我们将根据其架构，长度和类型的层次，以及分类任务的评估参数专注于最优惠的算法。首先，我们审查了一般用于分割的现有传统方法和深度学习架构。接下来，我们基于实施模型的精度和损耗功能进行深入的性能和分析。对结果的关键分析是突出了改进的所有重要问题。

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