University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Sector 16-C, Dwarka, New Delhi, India.
Department of Information Technology, Delhi Technological University, Shahbad Daulatpur, Rohini, New Delhi, India.
Sci Rep. 2022 Jul 13;12(1):11880. doi: 10.1038/s41598-022-15268-6.
CoViD19 is a novel disease which has created panic worldwide by infecting millions of people around the world. The last significant variant of this virus, called as omicron, contributed to majority of cases in the third wave across globe. Though lesser in severity as compared to its predecessor, the delta variant, this mutation has shown higher communicable rate. This novel virus with symptoms of pneumonia is dangerous as it is communicable and hence, has engulfed entire world in a very short span of time. With the help of machine learning techniques, entire process of detection can be automated so that direct contacts can be avoided. Therefore, in this paper, experimentation is performed on CoViD19 chest X-ray images using higher order statistics with iterative and non-iterative models. Higher order statistics provide a way of analyzing the disturbances in the chest X-ray images. The results obtained are quite good with 96.64% accuracy using a non-iterative model. For fast testing of the patients, non-iterative model is preferred because it has advantage over iterative model in terms of speed. Comparison with some of the available state-of-the-art methods and some iterative methods proves efficacy of the work.
新冠病毒是一种新型疾病，它已在全球范围内感染了数百万人，引发了恐慌。这种病毒的最后一个显著变体，即奥密克戎，是全球第三波疫情中大多数病例的罪魁祸首。虽然与之前的德尔塔变体相比，其严重程度较轻，但这种突变显示出更高的传染性。这种新型病毒具有肺炎症状，非常危险，因为它具有传染性，因此在很短的时间内席卷了整个世界。借助机器学习技术，可以实现整个检测过程的自动化，从而避免直接接触。因此，在本文中，我们使用迭代和非迭代模型对新冠病毒的胸部 X 射线图像进行了高阶统计分析。高阶统计为分析胸部 X 射线图像中的干扰提供了一种方法。使用非迭代模型可获得 96.64%的准确率，结果相当不错。为了快速测试患者，非迭代模型是首选，因为它在速度方面优于迭代模型。与一些现有的最先进的方法和一些迭代方法的比较证明了这项工作的有效性。
