论文标题
不同批次大小参数对预测Covid19情况的影响
Effect of Different Batch Size Parameters on Predicting of COVID19 Cases
论文作者
论文摘要
自2019年12月以来，新的冠状病毒2019年，也称为Covid19，是一种非常严重的流行病，已杀死了数千甚至数百万人。它被世界卫生组织在2020年3月被定义为大流行。它表示，该病毒通常是由滴水引起的滴水或通过互动的鸡巴触摸造成的，或者通过接触的鸡肉触摸而导致的。通过实时逆转录酶聚合酶链反应（RRT-PCR）测试检测到病毒的存在，借助从鼻子或喉咙中取出的拭子。另外，X射线和CT成像方法也用于支持此方法。由于已知RRT-PCR检验中的精度灵敏度较低，因此辅助诊断方法具有非常重要的位置。计算机辅助诊断和检测系统是开发的，尤其是在X射线和CT图像的帮助下。关于文献中Covid19检测的研究正在逐日增加。在这项研究中，使用属于4种不同不同的数据（病毒性肺炎，covid19，正常，细菌性肺炎）的类别，研究了不同批量大小（BH = 3、10、20、30、40和50）参数值对它们在检测CoVID19和其他类别的性能的影响。该研究是使用预先训练的RESNET50卷积神经网络进行的。根据获得的结果，他们在培训和测试数据上密切执行。但是，观察到，随着批次尺寸值的增加，测试数据中的稳态延迟。 BH = 3的最高COVID19检测为95.17％，而总准确性值为97.97％，BH = 20。根据发现，可以说批次尺寸值并不显着影响总体性能，但批次尺寸值的增加获得了稳定的结果。
The new coronavirus 2019, also known as COVID19, is a very serious epidemic that has killed thousands or even millions of people since December 2019. It was defined as a pandemic by the world health organization in March 2020. It is stated that this virus is usually transmitted by droplets caused by sneezing or coughing, or by touching infected surfaces. The presence of the virus is detected by real-time reverse transcriptase polymerase chain reaction (rRT-PCR) tests with the help of a swab taken from the nose or throat. In addition, X-ray and CT imaging methods are also used to support this method. Since it is known that the accuracy sensitivity in rRT-PCR test is low, auxiliary diagnostic methods have a very important place. Computer-aided diagnosis and detection systems are developed especially with the help of X-ray and CT images. Studies on the detection of COVID19 in the literature are increasing day by day. In this study, the effect of different batch size (BH=3, 10, 20, 30, 40, and 50) parameter values on their performance in detecting COVID19 and other classes was investigated using data belonging to 4 different (Viral Pneumonia, COVID19, Normal, Bacterial Pneumonia) classes. The study was carried out using a pre-trained ResNet50 convolutional neural network. According to the obtained results, they performed closely on the training and test data. However, it was observed that the steady state in the test data was delayed as the batch size value increased. The highest COVID19 detection was 95.17% for BH = 3, while the overall accuracy value was 97.97% with BH = 20. According to the findings, it can be said that the batch size value does not affect the overall performance significantly, but the increase in the batch size value delays obtaining stable results.
