Shape Recognition Using Unconstrained Pill Images Based on Deep Convolution Network

Suksawatchon, Ureerat; Srikamdee, Supawadee; Suksawatchon, Jakkarin; Werapan, Worawit

doi:10.1109/incit56086.2022.10067600

“…In healthcare, YOLO has been instrumental in assisting and improving diagnostic processes and treatment outcomes. The applications include, but are not limited to, cancer detection [31,32], skin segmentation [33], and pill identification [34,35] which showcase the model's ability to adapt to different needs, and essential 3 of 54 tasks. Surveillance and Security systems also leverage YOLO for real-time monitoring and rapid identification of suspicious activities [36,37].…”

Section: You Only Look Once Approachmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

4

0

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This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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“…In healthcare, YOLO has been instrumental in assisting and improving diagnostic processes and treatment outcomes. The applications include, but are not limited to, cancer detection [37,38], skin segmentation [39], and pill identification [40,41] which showcase the model's ability to adapt to different needs, and essential tasks.…”

Section: You Only Look Once Approachmentioning

confidence: 99%

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

Sapkota,

Qureshi,

Flores-Calero

et al. 2024

Preprint

0

View full text Add to dashboard Cite

This review systematically examines the progression of the You Only Look Once (YOLO) object detection algorithms from YOLOv1 to the recently unveiled YOLOv10. Employing a reverse chronological analysis, this study examines the advancements introduced by YOLO algorithms, beginning with YOLOv10 and progressing through YOLOv9, YOLOv8, and subsequent versions to explore each version's contributions to enhancing speed, accuracy, and computational efficiency in real-time object detection. The study highlights the transformative impact of YOLO across five critical application areas: automotive safety, healthcare, industrial manufacturing, surveillance, and agriculture. By detailing the incremental technological advancements that each iteration brought, this review not only chronicles the evolution of YOLO but also discusses the challenges and limitations observed in each earlier versions. The evolution signifies a path towards integrating YOLO with multimodal, context-aware, and General Artificial Intelligence (AGI) systems for the next YOLO decade, promising significant implications for future developments in AI-driven applications.

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“…However, the effectiveness of Guo et al's technique is limited by certain factors such as the lighting condition, the camera resolution, and the pill and background color contrast. Some pill recognition techniques have been developed to identify a pill based on only a subset of shape, color, and imprint, such as the works in [36][37][38]. The work in [39] identified pills that had only one of four pre-identified colors and classes.…”

Section: Related Workmentioning

confidence: 99%

CNN-Based Pill Image Recognition for Retrieval Systems

Al-Hussaeni

¹

,

Karamitsos

²

,

Adewumi

³

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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Shape Recognition Using Unconstrained Pill Images Based on Deep Convolution Network

Cited by 4 publications

References 11 publications

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

YOLOv10 to Its Genesis: A Decadal and Comprehensive Review of The You Only Look Once Series

CNN-Based Pill Image Recognition for Retrieval Systems

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