AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

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Publication Details

Published
2025-03-28
DOI
https://doi.org/10.1109/icdsaai65575.2025.11011875
Primary Topic
Pharmaceutical Quality and Counterfeiting
Type
article
Field-Weighted Citation Impact
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AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

Sivaiah Bellamkonda, Vikas Kumar, G. Rajeswarappa, M. Navya et al.
Pharmaceutical Quality and Counterfeiting
article

AutoMediVision:A Deep Learning System for Accurate Pharmaceutical pill identification

Sivaiah Bellamkonda, Vikas Kumar, G. Rajeswarappa, M. Navya, Shaik Sameer, Y. Lasya
article en

Abstract

The goal of this project is to build a high performance system that offers prediction of pill information from images, the pill name, medical use and recommended dosage by using MobileNet which is a lightweight and efficient deep learning model. This system serves as an important tool for healthcare professionals and patients as it offers a means of reducing risk because of the large number of medications out there. For this task, MobileNet was chosen for its high accuracy and good balance between computational efficiency and high accuracy, and hence can be deployed on mobile devices. Having trained the model on a wide variety of pills with class wise annotations, it generalized well for diverse pills. The output of the system is the pill name, primary medical indications and common dosage instructions that help in safer medication management by supporting healthcare providers in providing better care and patient safety.

G Pulla Reddy Dental College & Hospital (IN)
Openalex Percentile: Top 83%
Pharmaceutical Quality and Counterfeiting
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