COVID-19 Detection Using CT Image Based On YOLOv5 Network
Ruyi Qu, Yi Yang, Yuwei Wang
Abstract
The COVID-19 pandemic has broken down the global medical order tremendously, we urgently need an efficient treatment. 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 COVID-19. 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: one-stage and two stage. The representational model are Faster RCNN and YOLO series. Then in the section III we describe YOLOv5 model in the detail. Compared Experiment 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.