AI-driven drug pill recognition system: A CNN-based android application for visually impaired and senior citizens

As individuals age, challenges such as declining vision and memory can increase the risk of medication errors, particularly among the elderly and visually impaired. To address this issue, this research presents a deep learning-based Android application for accurate and accessible drug pill recognition. The system leverages a contrast-enhanced Convolutional Neural Network (CNN) trained on a diverse pill image dataset, achieving a test accuracy of 98%. Integrated with a REST API, the model enables real-time image classification via a smartphone camera. The application further enhances usability through voice-assisted feedback and visual pill details, promoting autonomy and medication adherence. This AI-driven solution bridges the gap between healthcare and technology, offering a practical tool to reduce medication errors and improve the quality of life for users with visual and cognitive impairments.

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Publication Details

Journal
i-manager s Journal on Mobile Applications and Technologies
Published
2025-01-01
DOI
https://doi.org/10.26634/jmt.12.1.22104
Primary Topic
Currency Recognition and Detection
Type
article
Field-Weighted Citation Impact
0.00
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article

AI-driven drug pill recognition system: A CNN-based android application for visually impaired and senior citizens

R Rashmi, Mahendra Handore Shubhangi
i-manager s Journal on Mobile Applications and Technologies
Currency Recognition and Detection
article

AI-driven drug pill recognition system: A CNN-based android application for visually impaired and senior citizens

R Rashmi, Mahendra Handore Shubhangi
article en

Abstract

As individuals age, challenges such as declining vision and memory can increase the risk of medication errors, particularly among the elderly and visually impaired. To address this issue, this research presents a deep learning-based Android application for accurate and accessible drug pill recognition. The system leverages a contrast-enhanced Convolutional Neural Network (CNN) trained on a diverse pill image dataset, achieving a test accuracy of 98%. Integrated with a REST API, the model enables real-time image classification via a smartphone camera. The application further enhances usability through voice-assisted feedback and visual pill details, promoting autonomy and medication adherence. This AI-driven solution bridges the gap between healthcare and technology, offering a practical tool to reduce medication errors and improve the quality of life for users with visual and cognitive impairments.

i-manager s Journal on Mobile Applications and TechnologiesVol. 12(1)
Trinity College (CA)
Openalex Percentile: Top 82%
Currency Recognition and Detection
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