论文标题
COVID-19胸部X射线分类的高参数优化
Hyperparameter Optimization for COVID-19 Chest X-Ray Classification
论文作者
论文摘要
尽管引入了疫苗，但冠状病毒疾病（Covid-19）仍然是全球困境，不断发展新的变种，例如Delta和最近的Omicron。当前测试标准是通过聚合酶链反应（PCR）。但是，对于许多人来说，PCR可能是昂贵，缓慢和/或无法访问的。另一方面，X射线自20世纪初以来就很容易使用，并且相对便宜，更快地获取，通常由健康保险覆盖。通过仔细选择模型，超参数和增强功能，我们表明可以在二进制分类中开发具有83％准确性的模型，而多级别为64％，用于检测胸部X射线的COVID-19感染。
Despite the introduction of vaccines, Coronavirus disease (COVID-19) remains a worldwide dilemma, continuously developing new variants such as Delta and the recent Omicron. The current standard for testing is through polymerase chain reaction (PCR). However, PCRs can be expensive, slow, and/or inaccessible to many people. X-rays on the other hand have been readily used since the early 20th century and are relatively cheaper, quicker to obtain, and typically covered by health insurance. With a careful selection of model, hyperparameters, and augmentations, we show that it is possible to develop models with 83% accuracy in binary classification and 64% in multi-class for detecting COVID-19 infections from chest x-rays.
