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首页> 外文期刊>Pattern Analysis and Applications >Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network
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Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network

机译：使用卷积神经网络的集合学习自动Covid-19检测X射线图像

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摘要

COVID-19 continues to have catastrophic effects on the lives of human beings throughout the world. To combat this disease it is necessary to screen the affected patients in a fast and inexpensive way. One of the most viable steps towards achieving this goal is through radiological examination, Chest X-Ray being the most easily available and least expensive option. In this paper, we have proposed a Deep Convolutional Neural Network-based solution which can detect the COVID-19 +ve patients using chest X-Ray images. Multiple state-of-the-art CNN models-DenseNet201, Resnet50V2 and Inceptionv3, have been adopted in the proposed work. They have been trained individually to make independent predictions. Then the models are combined, using a new method of weighted average ensembling technique, to predict a class value. To test the efficacy of the solution we have used publicly available chest X-ray images of COVID +ve and -ve cases. 538 images of COVID +ve patients and 468 images of COVID -ve patients have been divided into training, test and validation sets. The proposed approach gave a classification accuracy of 91.62% which is higher than the state-of-the-art CNN models as well the compared benchmark algorithm. We have developed a GUI-based application for public use. This application can be used on any computer by any medical personnel to detect COVID +ve patients using Chest X-Ray images within a few seconds.
机译：Covid-19继续对全世界人类生命具有灾难性影响。为了解决这种疾病，有必要以快速且廉价的方式筛选受影响的患者。实现这一目标的最可行步骤之一是通过放射性检查，胸部X射线是最容易获得的，最便宜的选择。在本文中，我们提出了一种深度卷积神经网络的解决方案，可以使用胸部X射线图像检测Covid-19 + ve患者。在拟议的工作中采用了多种最先进的CNN模型-DenSenet201，Resnet50v2和Inceptionv3。他们已被单独培训以进行独立预测。然后使用一种新的加权平均集合技术方法来组合模型，以预测类值。为了测试解决方案的功效，我们使用了Covid + VE和-VE案例的可公开胸部X射线图像。 538个Covid + ve患者的图像和468个Covid -ve患者的图像分为培训，测试和验证套装。所提出的方法给出了91.62％的分类精度，其高于最先进的CNN模型以及比较的基准算法。我们开发了一种基于GUI的申请，供公共使用。本申请可由任何医务人员在任何计算机上使用，以在几秒钟内使用胸部X射线图像检测Covid + ve患者。

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