  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_ADDRESS>of our YOLOv5s is 0.623
which is 0.157 and 0.101 higher than Faster RCNN and EfficientDet respectively.
