论文标题
一起对抗大流行：在层析成像图像上学习多个模型，以进行19诊断
Fighting together against the pandemic: learning multiple models on tomography images for COVID-19 diagnosis
论文作者
论文摘要
2020年人类面临的巨大挑战是与19日的战斗。全世界都在努力寻找一种有效的疫苗，以保护尚未感染的人。通过实时聚合酶链反应（RT-PCR）测试或胸腔计算机断层扫描（CT）扫描图像进行的替代溶液仍保持早期诊断。深度学习算法，特别是卷积神经网络，代表了图像分析的方法。他们优化了分类设计任务，这对于包括医疗在内的不同类型的图像的自动方法至关重要。在本文中，我们采用了预处理的深卷卷神经网络体系结构，以诊断CT图像上的Covid-19疾病。我们的想法的灵感来自于整个人类所取得的成就，这基本上的多种贡献比单一贡献要好于对抗大流行的单一贡献。首先，我们适应并随后重新训练，以假设其他应用领域中采用的一些神经体系结构。其次，我们将在整体分类环境中通过神经体系结构从图像中提取的知识结合在一起。实验阶段是在CT图像数据集上进行的，并且获得的结果表明，相对于最先进的竞争者，提出的方法的有效性。
The great challenge for the humanity of the year 2020 is the fight against COVID-19. The whole world is making a huge effort to find an effective vaccine with purpose to protect people not yet infected. The alternative solution remains early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) test or thorax computer tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for the image analysis. They optimize the classification design task, essential for an automatic approach on different types of images, including medical. In this paper, we adopt pretrained deep convolutional neural network architectures in order to diagnose COVID-19 disease on CT images. Our idea is inspired by what the whole of humanity is achieving, substantially the set of multiple contributions is better than the single one for the fight against the pandemic. Firstly, we adapt, and subsequently retrain, for our assumption some neural architectures adopted in other application domains. Secondly, we combine the knowledge extracted from images by neural architectures in an ensemble classification context. Experimental phase is performed on CT images dataset and results obtained show the effectiveness of the proposed approach with respect to state-of-the-art competitors.
