论文标题
DCNNV-19：用于COVID-19的深卷卷神经网络在胸部计算机断层扫描中检测
DCNNV-19: A Deep Convolutional Neural Network for COVID-19 Detection in Chest Computed Tomographies
论文作者
论文摘要
该技术报告建议在分析患有严重急性呼吸综合征症状（SARS）症状的胸部计算机断层扫描图像的初步诊断方法中使用深卷积神经网络作为初步的诊断方法，并怀疑COVID-19-COVID-19疾病，尤其是在RT-PCR延迟的情况下，可能会导致紧急护理的延迟，并导致严重的临时造成临时的伤害，或者长期造成临时伤害。该模型在83,391张图像上进行了培训，并在15,297张验证，并在22,185个数字上进行了测试，在Cohen's Kappa中获得了98％的F1分数，准确性98.4％，损失为5.09％。与当前的金色标准检查，实时反向转录酶聚合酶链反应（RT-PCR）相比，证明高度准确的自动分类并提供的时间更少。
This technical report proposes the use of a deep convolutional neural network as a preliminary diagnostic method in the analysis of chest computed tomography images from patients with symptoms of Severe Acute Respiratory Syndrome (SARS) and suspected COVID-19 disease, especially on occasions when the delay of the RT-PCR result and the absence of urgent care could result in serious temporary, long-term, or permanent health damage. The model was trained on 83,391 images, validated on 15,297, and tested on 22,185 figures, achieving an F1-Score of 98%, 97.59% in Cohen's Kappa, 98.4% in Accuracy, and 5.09% in Loss. Attesting a highly accurate automated classification and providing results in less time than the current gold-standard exam, Real-Time reverse-transcriptase Polymerase Chain Reaction (RT-PCR).
