Establishment and Initial Application of YOLO-V5 Deep Learning Prediction Model for Accurate Identification of Drug Packaging in Outpatient and Emergency Pharmacies
10.3870/j.issn.1004-0781.2024.04.030
- VernacularTitle:准确识别门急诊药品包装盒的YOLO-V5深度学习预测模型的建立及初步应用
- Author:
Yuqun WANG
1
;
Mingzhu MENG
Author Information
1. 南京医科大学附属常州第二人民医院 药剂科,常州 213164
- Keywords:
Pharmacies;
Dispensing error;
Deep learning;
Pharmacists
- From:
Herald of Medicine
2024;43(4):661-666,后插1
- CountryChina
- Language:Chinese
-
Abstract:
Objective To develop an accurate deep learning prediction model of YOLO-V5 capable of accurately iden-tifying medication packaging boxes in outpatient and emergency pharmacies,aiming to assist pharmacists in achieving"zero dis-pensing error".Methods A total of 2 560 images of packaging boxes from 136 different drugs were collected and labeled to form the deep learning dataset.The dataset was split into training and validation sets at a ratio of 4∶1.YOLO-V5 deep-learning algorithm was employed for training the data using images from our dataset(train epochs:500,batch size:4,learning rate:0.01).The values of the precision(Pr)and mean average precision(mAP)were used as measures for model performance evaluation.Results The Pr of the four sub-models of YOLO-V5 in the training set all reached 1.00.The mAP_0.5 of YOLO-V5x was 0.95,which was higher than those of YOLO-V5s(0.94),YOLO-V5l(0.94),and YOLO-V5m(0.94).The mAP_0.5:0.95 of YOLO-V5l and YOLO-V5x were 0.85 which were higher than those of YOLO-V5s(0.84)and YOLO-V5m(0.84).Training time and model size were 82.56 hours and 166.00MB for YOLO-V5x which were the highest among the four models.The speed of detection in one im-age was 11ms for YOLO-V5s which was the fastest among the four models.Conclusion YOLO-V5 can accurately identify the packaging of drugs in outpatient and emergency pharmacies.Implementing an artificial-intelligence-assisted drug dispensation sys-tem is feasible for pharmacists to achieve"zero dispensing error".
