论文标题
COVID-19使用基于Yolov5网络的CT图像检测
COVID-19 Detection Using CT Image Based On YOLOv5 Network
论文作者
论文摘要
计算机辅助诊断（CAD）提高了诊断效率，帮助医生提供了快速，自信的诊断，它在Covid19的治疗中发挥了重要作用。在我们的任务中，我们解决了有关异常检测和分类的问题。 Kaggle平台提供的数据集以及我们选择Yolov5作为模型。我们在相关工作部分中介绍了一些有关客观检测的方法，反对检测可以分为两个流：onestage和两个阶段。代表性模型是更快的RCNN和YOLO系列。然后，我们在细节中描述Yolov5模型。比较实验和结果在第四节中显示。我们选择平均平均精度（MAP）作为实验指标，而较高的（平均）地图是，该模型将获得越好的结果。我们的yolov5s的[email protected]分别为0.623，分别比更快的RCNN和EfficityDet高0.157和0.101。
Computer aided diagnosis (CAD) increases diagnosis efficiency, helping doctors providing a quick and confident diagnosis, it has played an important role in the treatment of COVID19. In our task, we solve the problem about abnormality detection and classification. The dataset provided by Kaggle platform and we choose YOLOv5 as our model. We introduce some methods on objective detection in the related work section, the objection detection can be divided into two streams: onestage and two stage. The representational model are Faster RCNN and YOLO series. Then we describe the YOLOv5 model in the detail. Compared Experiments and results are shown in section IV. We choose mean average precision (mAP) as our experiments' metrics, and the higher (mean) mAP is, the better result the model will gain. [email protected] of our YOLOv5s is 0.623 which is 0.157 and 0.101 higher than Faster RCNN and EfficientDet respectively.
