Abstract
BackgroundThe correct identification of pills is very important to ensure the safe administration of drugs to patients. Here, we use three current mainstream object detection models, namely RetinaNet, Single Shot Multi-Box Detector (SSD), and You Only Look Once v3(YOLO v3), to identify pills and compare the associated performance.MethodsIn this paper, we introduce the basic principles of three object detection models. We trained each algorithm on a pill image dataset and analyzed the performance of the three models to determine the best pill recognition model. The models were then used to detect difficult samples and we compared the results.ResultsThe mean average precision (MAP) of RetinaNet reached 82.89%, but the frames per second (FPS) is only one third of YOLO v3, which makes it difficult to achieve real-time performance. SSD does not perform as well on the indicators of MAP and FPS. Although the MAP of YOLO v3 is slightly lower than the others (80.69%), it has a significant advantage in terms of detection speed. YOLO v3 also performed better when tasked with hard sample detection, and therefore the model is more suitable for deployment in hospital equipment.ConclusionOur study reveals that object detection can be applied for real-time pill identification in a hospital pharmacy, and YOLO v3 exhibits an advantage in detection speed while maintaining a satisfactory MAP.
Highlights
In China, due to medical insurance policies requirements, oral pills for inpatients are dispensed individually by inpatient pharmacies according to the prescribed dosage, and pharmacists need to disassemble the packaging of the pills for dispensing
Comparison of algorithm detection results After training, the different algorithms were used for pill identification on the test set; the results are shown in Fig. 7 and Table 3
The detection speed of these two algorithms exceeds 30 frames per second (FPS), which is much faster than RetinaNet
Summary
In China, due to medical insurance policies requirements, oral pills for inpatients are dispensed individually by inpatient pharmacies according to the prescribed dosage, and pharmacists need to disassemble the packaging of the pills for dispensing. If the model calculation takes too long, it will not be suitable for use in a busy environment To investigate this possibility, we trained some current mainstream object recognition algorithms, including RetinaNet, Single Shot Multi-Box Detector (SSD), and You Only Look Once (YOLO v3), on a newly created pill dataset and compared the results in terms of accuracy and detection speed, to determine the best model to assist pharmacists and other healthcare workers dispense and check drugs affordably. We use three current mainstream object detection models, namely RetinaNet, Single Shot Multi-Box Detector (SSD), and You Only Look Once v3(YOLO v3), to identify pills and compare the associated performance
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