鉴于COVID-19继续对社会产生显著影响-感染7亿报告的个体,导致696万人死亡-许多深度学习工作最近都集中在病毒的诊断上。然而,由于缺乏大型数据集,评估严重性仍然是一个开放和具有挑战性的问题,要为其找到权重的图像的大维度,以及现代图形处理单元(GPU)的计算限制。在本文中,一个新的,迁移学习的迭代应用在3DCT扫描的COVID-19严重度分析领域得到了证明。此方法允许在MosMed数据集上增强性能,这是一个小而具有挑战性的数据集,包含1130张患者的5个级别的COVID-19严重程度(零,温和,中等,严重,和关键)。具体来说,鉴于输入图像的维数很大,我们创建了几个自定义的浅层卷积神经网络(CNN)架构,并迭代地细化和优化它们,注意学习率,图层类型,规范化类型,过滤器尺寸,dropout值,还有更多.经过初步的建筑设计,这些模型是在两个类的数据集构建模型的简化版本上进行系统训练的,然后是三等,然后是四等,最后是五类分类。简化的问题结构允许模型开始学习初步特征,然后可以进一步修改更困难的分类任务。我们的最终模型CoSev通过优化将分类精度从最初的60%以下提高到81.57%,在数据集上达到与最先进的性能相似的性能,更简单的安装程序。除了COVID-19的严重程度诊断,探索的方法可以应用于一般的基于图像的疾病检测。总的来说,这项工作强调了创新的方法,这些方法可以推进当前的高维计算机视觉实践,低样本数据以及数据驱动机器学习的实用性和特征设计对训练的重要性,然后可以实施以改善临床实践。
Given the pronounced impact COVID-19 continues to have on society-infecting 700 million reported individuals and causing 6.96 million deaths-many deep learning works have recently focused on the virus\'s diagnosis. However, assessing severity has remained an open and challenging problem due to a lack of large datasets, the large dimensionality of images for which to find weights, and the compute limitations of modern graphics processing units (GPUs). In this paper, a new, iterative application of transfer learning is demonstrated on the understudied field of 3D CT scans for COVID-19 severity analysis. This methodology allows for enhanced performance on the MosMed Dataset, which is a small and challenging dataset containing 1130 images of patients for five levels of COVID-19 severity (Zero, Mild, Moderate, Severe, and Critical). Specifically, given the large dimensionality of the input images, we create several custom shallow convolutional neural network (CNN) architectures and iteratively refine and optimize them, paying attention to learning rates, layer types, normalization types, filter sizes, dropout values, and more. After a preliminary architecture design, the models are systematically trained on a simplified version of the dataset-building models for two-class, then three-class, then four-class, and finally five-class classification. The simplified problem structure allows the model to start learning preliminary features, which can then be further modified for more difficult classification tasks. Our final model CoSev boosts classification accuracies from below 60% at first to 81.57% with the optimizations, reaching similar performance to the state-of-the-art on the dataset, with much simpler setup procedures. In addition to COVID-19 severity diagnosis, the explored methodology can be applied to general image-based disease detection. Overall, this work highlights innovative methodologies that advance current computer vision practices for high-dimension, low-sample data as well as the practicality of data-driven machine learning and the importance of feature design for training, which can then be implemented for improvements in clinical practices.