COVID-19 pandemic is a deadly disease spreading very fast. People with the confronted immune system are susceptible to many health conditions. A highly significant condition is pneumonia, which is found to be the cause of death in the majority of patients. The main purpose of this study is to find the volume of GGO and consolidation of a covid-19 patient so that the physicians can prioritize the patients. Here we used transfer learning techniques for segmentation of lung CTs with the latest libraries and techniques which reduces training time and increases the accuracy of the AI Model. This system is trained with DeepLabV3+ network architecture and model Resnet50 with Imagenet weights. We used different augmentation techniques like Gaussian Noise, Horizontal shift, color variation, etc to get to the result. Intersection over Union(IoU) is used as the performance metrics. The IoU of lung masks is predicted as 99.78% and that of infected masks is as 89.01%. Our work effectively measures the volume of infected region by calculating the volume of infected and lung mask region of the patients.
翻译:COVID-19大流行是一种致命的疾病,传播速度非常快。有免疫系统的人易受许多健康状况的影响。肺炎是一个非常严重的疾病,肺炎被认为是大多数病人死亡的原因。本研究的主要目的是找到GGO的量,并合并一个covid-19病人,以便医生能够确定病人的优先次序。我们在这里使用最新的图书馆和工艺转移学习技术,将肺部CT与减少培训时间和提高AI模型的准确性分开。这个系统是用DeepLabV3+网络架构和带有图像网重量的Resnet50模型来训练的。我们使用了不同的增强技术,如高山噪音、水平移动、颜色变化等,以达到这一结果。对联合(IoU)的交叉使用作为性能指标。肺罩的IoU预测值为99.78%,受感染的口罩值为89.01%。我们的工作通过计算病人受感染和肺罩区的数量,有效地测量了受感染地区的数量。