Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network
Automatic COVID-19 detection from X-ray images using ensemble learning with convolutional neural network
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DOI：
10.1007/s10044-021-00970-4
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发表时间：
2021-03-19
影响因子：
3.9
通讯作者：
Chakrabarti, Amlan
中科院分区：
文献类型：
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作者：
Das, Amit Kumar;Ghosh, Sayantani;Chakrabarti, Amlan
COVID-19 继续对全世界人类的生活造成灾难性影响。为了对抗这种疾病，有必要以快速且廉价的方式筛查受影响的患者。实现这一目标最可行的步骤之一是通过放射学检查，胸部 X 光检查是最容易获得且最便宜的选择。在本文中，我们提出了一种基于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 + 患者。所提出的工作中采用了多种最先进的 CNN 模型——DenseNet201、Resnet50V2 和 Inceptionv3。他们经过单独训练，能够做出独立的预测。然后使用加权平均集成技术的新方法组合模型来预测类别值。为了测试该解决方案的有效性，我们使用了公开的 COVID+ve 和 -ve 病例的胸部 X 光图像。 538 张新冠病毒阳性患者的图像和 468 张新冠病毒阴性患者的图像已分为训练集、测试集和验证集。所提出的方法的分类精度为 91.62%，高于最先进的 CNN 模型以及比较的基准算法。我们开发了一个基于 GUI 的应用程序供公众使用。任何医务人员都可以在任何计算机上使用此应用程序，在几秒钟内使用胸部 X 射线图像检测新冠肺炎患者。
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.