论文标题
临床实践：基于卷积神经网络的辅助诊断系统的设计和实施，用于COVID-19从胸部X射线图像进行案例检测
Towards Clinical Practice: Design and Implementation of Convolutional Neural Network-Based Assistive Diagnosis System for COVID-19 Case Detection from Chest X-Ray Images
论文作者
论文摘要
早期检测和随后评估肺部疾病发病率的关键工具之一是胸部射线照相。这项研究介绍了基于卷积神经网络（CNN）Carebot Covid应用程序的现实实现，以检测胸部X射线（CXR）图像的COVID-19。我们提出的模型采用简单直观的应用的形式。所使用的CNN可以作为Stow-RS预测端点部署，以直接实现DICOM查看器。这项研究的结果表明，基于Densenet和Resnet架构的深度学习模型可以从CXR图像中检测SARS-COV-2，精度为0.981，召回0.962，AP为0.993。
One of the critical tools for early detection and subsequent evaluation of the incidence of lung diseases is chest radiography. This study presents a real-world implementation of a convolutional neural network (CNN) based Carebot Covid app to detect COVID-19 from chest X-ray (CXR) images. Our proposed model takes the form of a simple and intuitive application. Used CNN can be deployed as a STOW-RS prediction endpoint for direct implementation into DICOM viewers. The results of this study show that the deep learning model based on DenseNet and ResNet architecture can detect SARS-CoV-2 from CXR images with precision of 0.981, recall of 0.962 and AP of 0.993.
