In the research, we developed a computer vision solution to support diagnostic radiology in differentiating between COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers. The chest radiograph appearance of COVID-19 pneumonia is thought to be nonspecific, having presented a challenge to identify an optimal architecture of a convolutional neural network (CNN) that would classify with a high sensitivity among the pulmonary inflammation features of COVID-19 and non-COVID-19 types of pneumonia. Rahman (2021) states that COVID-19 radiography images observe unavailability and quality issues impacting the diagnostic process and affecting the accuracy of the deep learning detection models. A significant scarcity of COVID-19 radiography images introduced an imbalance in data motivating us to use over-sampling techniques. In the study, we include an extensive set of X-ray imaging of human lungs (CXR) with COVID-19 pneumonia, influenza virus pneumonia, and normal biomarkers to achieve an extensible and accurate CNN model. In the experimentation phase of the research, we evaluated a variety of convolutional network architectures, selecting a sequential convolutional network with two traditional convolutional layers and two pooling layers with maximum function. In its classification performance, the best performing model demonstrated a validation accuracy of 93% and an F1 score of 0.95. We chose the Azure Machine Learning service to perform network experimentation and solution deployment. The auto-scaling compute clusters offered a significant time reduction in network training. We would like to see scientists across fields of artificial intelligence and human biology collaborating and expanding on the proposed solution to provide rapid and comprehensive diagnostics, effectively mitigating the spread of the virus
翻译:在研究中,我们开发了一个计算机远景解决方案,以支持诊断放射学,区分COVID-19肺炎、流感病毒肺炎和正常生物标志。COVID-19肺炎的胸部射线外观被认为并不具体,因为COVID-19肺炎的胸部射线外观被认为不具体,在确定一个动态神经网络的最佳结构方面提出了挑战,这种网络将高敏感地分类为COVID-19肺炎和非COVID-19型肺炎的肺炎性特征。拉赫曼(2021年)指出,COVID-19型射线图像观察到无法使用和质量问题,影响诊断过程,影响深层学习检测模型的准确性。由于严重缺乏COVID-19射线外观图像,导致数据不平衡,促使我们使用过度采样技术。在研究中,我们包括了一套广泛的人体肺X射线成像XR(CXR)肺炎、CVID-19肺炎和非COVID-19型肺炎等肺炎类型的肺炎和正常生物标志,以实现和准确的CNN模式。在研究试验阶段,我们评估了各种革命网络结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构结构,选择了两个阶段,在连续的不断递化阶段中,并进行最精确的递增分级化的递化轨道上,我们展示了正常的递化和亚分级的递化轨道上展示了基础。