使用胸部CT进行COVID-19分类的高效可视化卷积神经网络

截至2020年12月4日，新颖的2019年冠状病毒病（COVID-19）已感染全球6500万人，使世界濒临社会和经济崩溃的边缘。随着病例的迅速增加，深度学习已成为一种很有前途的诊断技术。..

Efficient and Visualizable Convolutional Neural Networks for COVID-19 Classification Using Chest CT

The novel 2019 coronavirus disease (COVID-19) has infected over 65 million people worldwide as of December 4, 2020, pushing the world to the brink of social and economic collapse. With cases rising rapidly, deep learning has emerged as a promising diagnosis technique.However, identifying the most accurate models to characterize COVID-19 patients is challenging because comparing results obtained with different types of data and acquisition processes is non-trivial. In this paper, we evaluated and compared 40 different convolutional neural network architectures for COVID-19 diagnosis, serving as the first to consider the EfficientNet family for COVID-19 diagnosis. EfficientNet-B5 is identified as the best model with an accuracy of 0.9931+/-0.0021, F1 score of 0.9931+/-0.0020, sensitivity of 0.9952+/-0.0020, and specificity of 0.9912+/-0.0048. Intermediate activation maps and Gradient-weighted Class Activation Mappings offer human-interpretable evidence of the model's perception of ground-class opacities and consolidations, hinting towards a promising use-case of artificial intelligence-assisted radiology tools.