论文标题
一个光CNN，用于从胸部的CT扫描中检测COVID-19
A Light CNN for detecting COVID-19 from CT scans of the chest
论文作者
论文摘要
OVID-19是一种全球疾病，已被世界卫生组织宣布为大流行。胸部的计算机断层扫描（CT）成像似乎是一种有效的诊断工具，可及时检测COVID-19并控制疾病的传播。深度学习已在医学成像中广泛使用，卷积神经网络（CNN）也已用于CT图像的分类。我们提出了一种基于挤压模型的轻型CNN设计，以有效地歧视与其他CT图像（社区获得的肺炎和/或健康图像）对COVID-19 CT图像有效歧视。在经过测试的数据集上，提出的修改后的Squeezenet CNN达到了83.00 \％的准确性，85.00 \％的敏感性，81.00 \％的特异性，81.73％的精度为81.73 \占0.83333的F1Score，以非常有效的方式（7.81秒秒的中等级别laptot）。除性能外，对于更复杂的CNN设计，平均分类时间非常有竞争力，因此也可以在中型计算机上其可用性。在下一个将来，我们旨在通过两个方向提高方法的性能：1）通过增加培训数据集（一旦可以使用其他CT图像）； 2）引入有效的预处理策略。
OVID-19 is a world-wide disease that has been declared as a pandemic by the World Health Organization. Computer Tomography (CT) imaging of the chest seems to be a valid diagnosis tool to detect COVID-19 promptly and to control the spread of the disease. Deep Learning has been extensively used in medical imaging and convolutional neural networks (CNNs) have been also used for classification of CT images. We propose a light CNN design based on the model of the SqueezeNet, for the efficient discrimination of COVID-19 CT images with other CT images (community-acquired pneumonia and/or healthy images). On the tested datasets, the proposed modified SqueezeNet CNN achieved 83.00\% of accuracy, 85.00\% of sensitivity, 81.00\% of specificity, 81.73\% of precision and 0.8333 of F1Score in a very efficient way (7.81 seconds medium-end laptot without GPU acceleration). Besides performance, the average classification time is very competitive with respect to more complex CNN designs, thus allowing its usability also on medium power computers. In the next future we aim at improving the performances of the method along two directions: 1) by increasing the training dataset (as soon as other CT images will be available); 2) by introducing an efficient pre-processing strategy.
