The coronavirus continues to disrupt our everyday lives as it spreads at an exponential rate. It needs to be detected quickly in order to quarantine positive patients so as to avoid further spread. This work proposes a new convolutional neural network (CNN) architecture called 'slow Encoding CNN. The proposed model's best performance wrt Sensitivity, Positive Predictive Value (PPV) found to be SP=0.67, PP=0.98, SN=0.96, and PN=0.52 on AI AGAINST COVID19 - Screening X-ray images for COVID-19 Infections competition's test data samples. SP and PP stand for the Sensitivity and PPV of the COVID-19 positive class, while PN and SN stand for the Sensitivity and PPV of the COVID-19 negative class.
翻译:冠状病毒以指数速率传播,继续扰乱我们的日常生活。 需要快速检测, 以隔离阳性病人, 以避免进一步扩散。 这项工作提出了一个新的神经神经神经网络(CNN)结构, 名为“ 低编码CNN ” 。 拟议的模型的最佳性能敏度、 阳性预测值(PPV) 为SP=0. 67, PP=0. 98, SN=0. 96, 和 PN=0.52 AI 对抗COVID19 - 为COVID-19感染竞赛测试数据样本筛选X光图像。 SP 和 PP 代表COVID-19 阳性等级的感官和PPV, 而 PN 和 SN 则代表COVID-19 负性等级的感官和PPV。