Automatic drug pills detection based on enhanced feature pyramid network and convolution neural networks

Abstract

Drug pill detection is one of the most important tasks in medication safety. The correct identification of drug based on the visual appearance is a key step towards the improvement of medication safety. Previous studies have aimed to recognise a drug based on the front or back view of the drug under a fixed viewing angle. In cases with multiple drugs and randomly placed drugs, the previous methods have difficulties in detecting and recognising different drugs in practical applications. A convolution neural network‐based detector is proposed in this work to overcome the difficulties and to assist patients in drug identification. The proposed system includes a localisation stage and a classification stage. The enhanced feature pyramid network (EFPN), is proposed for drug localisation, and Inception‐ResNet v2 is used in drug classification. The proposed Drug Pills Image Database contains a collection of 612 categories of drug datasets for deep learning research in the pharmaceutical field. The proposed EFPN achieves over 96% accuracy in the localisation experiment. In the complete system evaluation, the proposed system has obtained the Top‐1, Top‐3, and Top‐5 accuracies of 82.1, 92.4, and 94.7%, respectively.

Keywords

• automatic drug pills detection
• enhanced feature pyramid network
• convolution neural networks
• Drug pill detection
• medication safety
• fixed viewing angle