<i>MobileDeepPill</i>

Zeng, Xiao; Cao, Kai; Zhang, Mi

doi:10.1145/3081333.3081336

Cited by 80 publications

(9 citation statements)

References 14 publications

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“…Several techniques have been developed for pill image recognition with different accuracy levels and limitations [32,33]. MobileDeepPill [34] is a CNN architecture that integrates pill color, gradients, and shape measurements to compare between consumer and reference images. Guo et al [35] used a support vector machine (SVM) to study the color property, wherein they achieved a 97.90% overall color classification accuracy.…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Pill Image Recognition for Retrieval Systems

et al. 2023

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Medication should be consumed as prescribed with little to zero margins for errors, otherwise consequences could be fatal. Due to the pervasiveness of camera-equipped mobile devices, patients and practitioners can easily take photos of unidentified pills to avert erroneous prescriptions or consumption. This area of research goes under the umbrella of information retrieval and, more specifically, image retrieval or recognition. Several studies have been conducted in the area of image retrieval in order to propose accurate models, i.e., accurately matching an input image with stored ones. Recently, neural networks have been shown to be effective in identifying digital images. This study aims to provide an enhancement to image retrieval in terms of accuracy and efficiency through image segmentation and classification. This paper suggests three neural network (CNN) architectures: two models that are hybrid networks paired with a classification method (CNN+SVM and CNN+kNN) and one ResNet-50 network. We perform various preprocessing steps by using several detection techniques on the selected dataset. We conduct extensive experiments using a real-life dataset obtained from the National Library of Medicine database. The results demonstrate that our proposed model is capable of deriving an accuracy of 90.8%. We also provide a comparison of the above-mentioned three models with some existing methods, and we notice that our proposed CNN+kNN architecture improved the pill image retrieval accuracy by 10% compared to existing models.

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Section: Related Workmentioning

confidence: 99%

CNN-Based Pill Image Recognition for Retrieval Systems

et al. 2023

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“…Even though the competition used reference images that were photographed in a professionally supervised setting, the accuracy of drug recognition was not very high. Since the quality of a picture taken by a smartphone can be greatly influenced by illumination (lighting), shading, and background color, it is difficult to develop a system for image recognition [8]. Pill colors are especially affected by lighting hues and fluorescent light (Figure 2).…”

Section: Introductionmentioning

confidence: 99%

Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach

Cha¹,

Woo²,

Park³

et al. 2021

JMIR Med Inform

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Background Pill image recognition systems are difficult to develop due to differences in pill color, which are influenced by external factors such as the illumination from and the presence of a flash. Objective In this study, the differences in color between reference images and real-world images were measured to determine the accuracy of a pill recognition system under 12 real-world conditions (ie, different background colors, the presence and absence of a flash, and different exposure values [EVs]). Methods We analyzed 19 medications with different features (ie, different colors, shapes, and dosages). The average color difference was calculated based on the color distance between a reference image and a real-world image. Results For images with black backgrounds, as the EV decreased, the top-1 and top-5 accuracies increased independently of the presence of a flash. The top-5 accuracy for images with black backgrounds increased from 26.8% to 72.6% when the flash was on and increased from 29.5% to 76.8% when the flash was off as the EV decreased. However, the top-5 accuracy increased from 62.1% to 78.4% for images with white backgrounds when the flash was on. The best top-1 accuracy was 51.1% (white background; flash on; EV of +2.0). The best top-5 accuracy was 78.4% (white background; flash on; EV of 0). Conclusions The accuracy generally increased as the color difference decreased, except for images with black backgrounds and an EV of −2.0. This study revealed that background colors, the presence of a flash, and EVs in real-world conditions are important factors that affect the performance of a pill recognition model.

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“…According to the above literary works, the shape, colour, and imprint of a drug are the key characteristics for feature extraction. Previous research works have all focused on single drug classification based on the features at the topside of the pill, and the pill regions within an image have been determined manually or by limiting the background colour to black [5][6][7][8][9].…”

Section: Introductionmentioning

confidence: 99%

“…In conventional drug identification, the user tries to determine an unknown drug by manually entering the drug features on the drug website, which is a time-consuming process. In previous studies, several problems have not been effectively resolved in drug recognition [5][6][7][8][9], such as the random placements of drugs and the presence of multiple drugs in an image. The angle of drug rotation is also difficult to determine and normalise for each drug category.…”

Section: Introductionmentioning

confidence: 99%

“…Zeng et al [6] proposed the MoblieDeepPill, which used the Histogram of Oriented Gradient and Support Vector Machines (SVMs) for single drug localisation and the multi-CNN models were applied to collect pill characteristics. Wang et al [7] used three GoogLeNet Inception models with different effects on the colour, shape, and feature, and decision fusion was used to combine those models.…”

Section: Introductionmentioning

confidence: 99%

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

Tsai

Zhou

et al. 2020

IET Computer Vision

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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.

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MobileDeepPill

Cited by 80 publications

References 14 publications

CNN-Based Pill Image Recognition for Retrieval Systems

CNN-Based Pill Image Recognition for Retrieval Systems

Effects of Background Colors, Flashes, and Exposure Values on the Accuracy of a Smartphone-Based Pill Recognition System Using a Deep Convolutional Neural Network: Deep Learning and Experimental Approach

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

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