基于CNN架构的x射线Covid-19检测深度观察

Partho Ghose, U. Acharjee, Md. Amirul Islam, Selina Sharmin, Md. Ashraf Uddin
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引用次数: 5

摘要

Covid-19冠状病毒已成为一种严重的、危及生命的疾病,因其最有可能感染而在全球流行。自动协议系统是阻止covid - 19传播的一个令人信服的想法。本文旨在利用卷积神经网络(CNN)支持的深度学习模型来促进胸部x光片的自动诊断。起草时使用2875张covid - 19图像和10293张识别covid - 19计数的x射线图像作为数据集。从实验结果可以看出,所提出的结构达到96%的特异性、97%的AUC、96%的准确度、96%的灵敏度和96%的f1评分。因此,该系统的结果将有助于临床医生和研究人员发现COVID-19患者,并促进COVID-19患者的治疗。
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Deep Viewing for Covid-19 Detection from X-Ray Using CNN Based Architecture
The Covid-19 coronavirus has turned into a serious, life-threatening disease that is prevalent worldwide as it is most likely to infect. An automated protocol system is a compelling idea to stop the spread of covid19. This article aims at a deep learning model supported by a convolutional neural network (CNN) to facilitate automatic diagnosis from chest X-rays. A collection of 2875 covid19 images and 10293 X-ray pictures to recognize covid19 counts is being used as the data set for the drafting. From the experimental results, it can be seen that the proposed structure achieves 96% specificity, 97% AUC 96% accuracy, 96 % sensitivity, and 96 % F1-score. Therefore, the results of the proposed system will help clinicians and researchers discover COVID-19 patients and facilitate the treatment of COVID-19 patients.
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