Lighting Variability Correction for Pill Identification

Kang, Seungtae; Jang, Gil-Jin

doi:10.1109/icce-asia.2018.8552143

Cited by 2 publications

(1 citation statement)

References 7 publications

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“…They suggested an algorithm for removing lighting effects from pill and tablet recognition that is appropriate for preprocessing. Since the context of the captured pill images was so broad, they used the class activation map (CAM), a weakly controlled localization technique, to approximate the direction of illumination and find the location of the pills (9) . https://www.indjst.org/ Y. ou et al suggested that deep learning and Convolution Neural Network (CNN) can be used to replace repetitive screening work and increase drug screening reliability.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

Shashidhar¹,

Sahana²,

Chakraborty³

et al. 2021

IJST

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Objectives: To create a tablet recognition system that recognizes and gives an audio output of the name of the tablet so that the visually impaired person may recognize it. Methods: In this, to create a user friendly system, such that a blind person does not require the help of another person to use it. These systems give medical assistive instrument to take the drug at the accurate time as per the doctor prescription and removes dependency on others for the visually challenged people. Various models were technologically advanced to compact with misidentification of remedies but are incompetent of formative exact pill picked by the person. Findings: The name of the medicine is obtained from the image. Then the name of the medicine is converted from text to speech using Google text to speech (gTTS). The proposed system will be helpful for visually challenged people, SQLite Database management system has been used along with Scale Invariant Feature Transform algorithm (SIFT) to identify and pronounce local features in the image. The image of the medicine given by the user is matched with the images in the database. Novelty: Developed a user-friendly voice based tablet recognition system for visually impaired people and this system is fast and accurate.

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Section: Introductionmentioning

confidence: 99%

Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

Shashidhar¹,

Sahana²,

Chakraborty³

et al. 2021

IJST

View full text Add to dashboard Cite

show abstract

Artificial Intelligence-Enabled Medicine Dispensing: From Concepts to Practice in the Community Pharmacy (Preprint)

Barata¹,

Mendes²,

Santos³

et al. 2022

Preprint

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BACKGROUND Community pharmacies are increasingly exploring the opportunities of digital transformation. This paper proposes a system based on computer vision to assist pharmacists in medication dispensing and check adherence to medication plans. OBJECTIVE The proposed solution uses artificial intelligence (AI), cloud, and mobile technology to support pharmacists in managing patient medication records, identifying possible medication errors when preparing pillboxes, and providing personalized information to end-users. METHODS Action design research was selected to design a technology readiness level 7 solution in a community pharmacy. RESULTS For theory, the project suggests design guidelines for AI-enabled medicine dispensing and evaluates success factors for community pharmacy digital transformation. For practice, this paper presents an example of low-cost intelligent medication dispensing suitable for nursing homes and patients with pill medication plans. AI-enabled systems can contribute to (1) prevent errors in filling pillbox compartments, (2) provide an additional cross-check in medication dispensing, and (3) identify medication adherence problems in more demanding scenarios of institutions with multiple patients. CONCLUSIONS Community pharmacies must have an active role in adopting artificial intelligence in their processes, creating their digital transformation strategy, not limiting their choices to the technology providers offer.

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Lighting Variability Correction for Pill Identification

Cited by 2 publications

References 7 publications

Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

Recognition of Tablet using Blister Strip for Visually Impaired using SIFT Algorithm

Artificial Intelligence-Enabled Medicine Dispensing: From Concepts to Practice in the Community Pharmacy (Preprint)

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