Construction of classification model for oral medication in inpatient departments based on deep learning

WANG Xiyu; LI Nanxin; XIANG Fan; LI Yang; TANG Liangyou; XIANG Junlian; ZHANG Junran

doi:10.12102/j.issn.1009-6493.2024.06.002

Abstract：Objective:To construct an oral medication classification model for inpatient pharmacies based on deep learning.Methods:Actual scenes were simulated,95 types of pill picture were collected to construct dataset,and pictures in dataset were preprocessed.Classification model for pills was constructed based on MobileNet V2 network,and Squeeze-and-excitation networks were embeded in the model to enhance the network's feature channel dependencies.Method of transfer learning was applied,using the pill dataset built by researchers to train and test the model,and the performance of the model was tested through the classification accuracy and the parameter quantity of the model.Results:The constructed model in this study demonstrates outstanding performance in classifying oral pill images collected in natural environments.Trained and tested on a self-built dataset comprising 95 categories and a total of 728 images,the accuracy of model classification was 95.8%.This outperforms MobileNet V2,ShuffleNet V2,and ResNet50 by 11.6%,14.3%,and 11.3%,respectively.The amount of model parameters was 2.55 M,which was approximately 1 out of 10 of ResNet50.Conclusions:The model constructed in this paper better balances the complexity and classification accuracy of the model,and provides a technical route and effect verification for the automatic pill classification system involved in pharmacies and other scenarios.It has certain theoretical and practical application value for improving the level of nursing automation in specific situations such as pharmacy dispensing and ward drug distribution.