Pharmaceutical Blister Package Identification Based on Induced Deep Learning

Yun Han,Shun-Feng Su,Qiang Xiao,Sheng-Luen Chung,Jing-Syuan Wang

doi:10.1109/access.2021.3097181

Abstract

Prescription dispensing accuracy is of paramount importance for all hospitals. However, human errors are inevitable due to multiple reasons, such as fatigue, stress, heavy workload, lack of effective verification measures, mismanagement. Such human errors pose serious safety and health concerns on the part of patients and may as well lead to a series of medical disputes. Based on induced deep learning, this paper proposes a real-time Blister Package Identification System (BPIS) to assist pharmacists’ drug verification and dispensing. Under the guidance of the induction strategy, image preprocessing is introduced to form a standardized image containing the front and back side of the blister package, which is subsequently sent to CNN-based object identification network for feature extraction and identification. This preprocessing method allows the identification system to promote the deep learning system to focus on feature learning to obtain more information about the appearance of the package ruling out confounding factors such as background noise, size, shape or positioning. In addition, this article collects and establishes an image dataset of adult lozenges. Under this dataset, this paper verifies the enhancement of Induced Deep Learning (IDL) on YOLO v2, ResNet, and SENet. By optimizing the deep learning identification network with the help of the embedded technology and a two-side extraction mechanism, a real-time BPIS is built. Long-term tests in hospitals prove the effectiveness of the proposed system.

Highlights

Pharmacy station is an essential department in every hospital, and its core duty is to dispense the prescribed drugs to patients [1]
To solve the above problems, this paper proposes Induced Deep Learning (IDL) to improve identification accuracy and constructs a real-time Blister Package Identification System (BPIS) for drug verification by adopting an embedded technology
The original images are mainly used to test the object detection algorithm such as YOLO; the tailored images are mainly used to test the object identification algorithm such as ResNet, and the re-oriented two-sided image (RTI) are mainly used to test the effect of the induction method in different identification algorithms

Summary

Introduction

Pharmacy station is an essential department in every hospital, and its core duty is to dispense the prescribed drugs to patients [1]. Accurate dispensing in line with medical norms is the benchmark of pharmacy work. Any mistake would cause serious consequences, sometimes even resulting in irretrievable consequences [1]. To improve the dispensing accuracy, caution and technical tools are necessary in pharmacists’ work. Some hospitals use bar code dispensing system [2]. When pharmacists get the prescription, they would first scan the bar code on the patient’s medicine bag, and dispense the drug according to the information displayed on the screen, such as the name, storage location, code, appearance

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Conclusion