论文标题
使用CT扫描图像和预训练的模型诊断Covid-19疾病诊断
Diagnosis of COVID-19 disease using CT scan images and pre-trained models
论文作者
论文摘要
Covid-19的诊断对于预防和控制该疾病是必要的。深度学习方法已被认为是一种快速准确的方法。在本文中，通过三个众所周知的预训练网络的平行组合，我们试图将感染的冠状病毒感染样品与健康样本区分开。负模样损耗函数已用于模型训练。 SARS-COV-2数据集中的CT扫描图像用于诊断。 SARS-COV-2数据集包含2482张肺CT扫描图像，其中1252张图像属于COVID-19感染的样品。所提出的模型接近97％的准确性。
Diagnosis of COVID-19 is necessary to prevent and control the disease. Deep learning methods have been considered a fast and accurate method. In this paper, by the parallel combination of three well-known pre-trained networks, we attempted to distinguish coronavirus-infected samples from healthy samples. The negative log-likelihood loss function has been used for model training. CT scan images in the SARS-CoV-2 dataset were used for diagnosis. The SARS-CoV-2 dataset contains 2482 images of lung CT scans, of which 1252 images belong to COVID-19-infected samples. The proposed model was close to 97% accurate.
