基于胸片和迁移学习的CNNCD筛查模型鉴别新冠病毒

P. Parwekar, S. Rapeti, P. Vats, M. Sharma
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摘要

世界卫生组织已宣布冠状病毒疾病为一种流行病,截至目前,已有2683536人因新冠肺炎而丧生。减少病例的唯一方法是隔离新冠肺炎检测呈阳性的患者。研究人员设计了不同类型的深度学习模型来筛选新冠肺炎大流行。他们发现了不同的深度学习模型,使用胸部X射线检测新冠肺炎——大多数方法的准确率较低。在少数模型中,过度拟合问题在大多数模型中增加了难度。本文建立了一个自动新冠肺炎筛查模型,用于识别新冠肺炎检测、肺炎和正常。分别使用不同的学习技术来学习模型,如CNN、VGG16和ResNet。从这三种型号来看,VGG-16提供了更好的性能。©2021卡拉德尼兹工业大学。保留所有权利。
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CNNCD screening model to distinguish covid-virus by using chest X-ray and transfer learning
Coronavirus disease has been announced as a pandemic by World Health Organization and till this date 2,683,536 are lost their lives due to Covid-19. The one and only way to reduce the cases is Quarantine the patients that who are tested Covid-19 positive. Researchers have done Different kind of design deep learning models to screen the Covid-19 pandemic. They discovered different deep learning models to detect the Covid-19 using chest X-Rays most of the methods having less accuracy rate. In few models Overfitting problem increasing difficulties in most of the models. In this Article an automatic Covid-19 Screening model is developed to identify the Covid Detection, Pneumonia and Normal. Different learning techniques used separately to learn the model like CNN, VGG16 and ResNet. From those three models VGG-16 is giving better performance. © 2021 Karadeniz Technical University. All rights reserved.
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