Identification of Partially Occluded Pharmaceutical Blister Packages

Chung, Sheng‐Luen; Chen, Chih-Fang; Hsu, Gee-Sern; Wu, Shen-Te

doi:10.1109/avss.2019.8909890

Cited by 5 publications

(3 citation statements)

References 14 publications

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“…However, it must be realized that the training of machine learning models requires large datasets that are composed of a wide variety of various types of blisters, requiring a much larger database. Different deep learning approaches for the identification of blister packages have been presented by Wang et al [26] and Chung et al [27]. Their datasets included over 17,000 and 30,000 images of over 200 different blister types.…”

Section: Discussionmentioning

confidence: 99%

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Holtkötter

Amaral

Almeida

et al. 2022

Sensors

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Long-term adherence to medication is of critical importance for the successful management of chronic diseases. Objective tools to track oral medication adherence are either lacking, expensive, difficult to access, or require additional equipment. To improve medication adherence, cheap and easily accessible objective tools able to track compliance levels are necessary. A tool to monitor pill intake that can be implemented in mobile health solutions without the need for additional devices was developed. We propose a pill intake detection tool that uses digital image processing to analyze images of a blister to detect the presence of pills. The tool uses the Circular Hough Transform as a feature extraction technique and is therefore primarily useful for the detection of pills with a round shape. This pill detection tool is composed of two steps. First, the registration of a full blister and storing of reference values in a local database. Second, the detection and classification of taken and remaining pills in similar blisters, to determine the actual number of untaken pills. In the registration of round pills in full blisters, 100% of pills in gray blisters or blisters with a transparent cover were successfully detected. In the counting of untaken pills in partially opened blisters, 95.2% of remaining and 95.1% of taken pills were detected in gray blisters, while 88.2% of remaining and 80.8% of taken pills were detected in blisters with a transparent cover. The proposed tool provides promising results for the detection of round pills. However, the classification of taken and remaining pills needs to be further improved, in particular for the detection of pills with non-oval shapes.

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Section: Discussionmentioning

confidence: 99%

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Holtkötter

Amaral

Almeida

et al. 2022

Sensors

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show abstract

“…In order to evaluate the proposed algorithm, this paper compares the recognition rate of the proposed method with that of the commonly used pharmaceutical blister package identification method, as shown in Table 7. It can be seen from the table that the effect of directly using Yolo is 89.39%, relatively poor among all the tested methods; ResNet, SeNet, RIN [19] and Fast ROR [21] produces better results, higher than 90%, which may be satisfactory for ordinary object recognition. However, for demanding recognition rate applications such as drug recognition, further improvement is needed.…”

Section: Comparison With Other State-of-the-art Algorithmsmentioning

confidence: 96%

“…In recent years, methods based on deep learning have been mainly used. For example, Chung et al [19] used deep learning-based methods to realize blister package identification with occlusion, and Chung et al [20] proposed an end-to-end rapid identification method, which has achieved better results. For better results, Chung et al [21] just built a more compact structure based on the one-side image of the blister package to realize the rapid identification at the expense of slight reduction in identification accuracy.…”

Section: Related Workmentioning

confidence: 99%

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

et al. 2021

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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.INDEX TERMS Blister package identification, deep learning, induction, dispensing error, CNN.

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End-to-end identification of pharmaceutical blister packages based on one-side handheld images

Chung

Cho

2020

2020 International Conference on System Science and Engineering (ICSSE)

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Identification of Partially Occluded Pharmaceutical Blister Packages

Cited by 5 publications

References 14 publications

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Development and Validation of a Digital Image Processing-Based Pill Detection Tool for an Oral Medication Self-Monitoring System

Pharmaceutical Blister Package Identification Based on Induced Deep Learning

End-to-end identification of pharmaceutical blister packages based on one-side handheld images

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