COVID-19 Detection Using CT Image Based On YOLOv5 Network

TL;DR

This paper presents a COVID-19 detection method using YOLOv5 on CT images, demonstrating improved accuracy over other models like Faster RCNN and EfficientDet.

Contribution

The study applies YOLOv5 to COVID-19 CT image detection, achieving higher mean average precision compared to traditional object detection models.

Findings

YOLOv5 achieved mAP@0.5 of 0.623

YOLOv5 outperformed Faster RCNN and EfficientDet in accuracy

The method enhances rapid COVID-19 diagnosis using CT images

Abstract

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. mAP@0.5 of our YOLOv5s is 0.623 which is 0.157 and 0.101 higher than Faster RCNN and EfficientDet respectively.