基于CNN的胸部x线图像检测Covid-19疾病

Ajay Reddy Yeruva, Pragati Choudhari, Anurag Shrivastava, Devvret Verma, Sanchita Shaw, A. Rana
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引用次数: 7

摘要

Covid是一种最终导致死亡的呼吸道疾病。确诊与否至关重要。自2019年12月首次出现以来,COVID-19大流行一直是世界各地的一个问题。对于可能感染COVID-19的个人来说,及时准确的诊断对于接受治疗是绝对必要的。为了阻止COVID-19的流行,胸部x光需要能够使用机器学习对病毒进行独立诊断。本研究提供的证据表明,使用集成深度迁移学习进行COVID-19患者的早期诊断是有效和高效的。如果您按照这些说明行事,您将能够报告疑似COVID-19活动,并在有回复时收到回复。借助医疗传感器和云服务器的深度集成模型,胸部x射线模式可以识别感染的存在。本研究的作者通过使用胸部x射线图像作为训练数据,训练卷积神经网络系统可靠地预测Covid-19。研究人员是开发CNN算法的人。在模型的创建和培训过程中,他们遇到了困难,他们解决并开发了解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Covid-19 Disease Detection using Chest X-Ray Images by Means of CNN
Covid is a respiratory disease that ultimately results in death. It is of the utmost importance to determine whether or not a person has covid. Since it first appeared in December 2019, the COVID-19 pandemic has been a problem all across the world. For individuals who may have COVID-19, getting a timely and accurate diagnosis is absolutely necessary in order to receive medical treatment. In order to put a stop to the COVID-19 epidemic, chest X-rays will need to be capable of making an independent diagnosis of the virus using Machine Learning. This study provides evidence that the use of ensemble deep transfer learning for the early diagnosis of COVID-19 patients is both effective and efficient. If you follow these instructions, you will be able to report suspected COVID-19 activity and receive responses as they become available. With the help of medical sensors and the deep ensemble model of a cloud server, chest X-ray modalities can identify the presence of an infection. The authors of this study educated a Convolutional Neural Network system to reliably predict Covid-19 by using chest X-ray images as their training data. The researchers were the ones who developed the CNN algorithm. During the model's creation and training, they encountered difficulties, which they addressed and developed solutions for.
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