Automatic drug pills detection based on enhanced feature pyramid network and convolution neural networks. Issue 1 (20th January 2020)

• Record Type: Journal Article
• Authors: Ou, Yang‐Yen; Tsai, An‐Chao; Zhou, Xuan‐Ping; Wang, Jhing‐Fa
• Is Part Of: IET computer vision. Volume 14:Issue 1(2020)
• Page Start: 9
• Page End: 17
• Publication Date: 2020-01-20
• Subjects: neural nets -- image classification -- medical computing -- feature extraction -- drugs -- learning (artificial intelligence); automatic drug pills detection -- enhanced feature pyramid network -- convolution neural networks -- Drug pill detection -- medication safety -- fixed viewing angle -- multiple drugs -- randomly placed drugs -- different drugs -- convolution neural network-based detector -- drug identification -- drug localisation -- drug classification -- Drug Pills Image Database -- drug datasets; Computer vision -- Periodicals; Pattern recognition systems -- Periodicals; 006.37
• DOI: 10.1049/iet-cvi.2019.0171
• Languages: English
• ISSNs: 1751-9632

Abstract

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.