Medicine Recognition and Classification

The rapid growth of digital healthcare solutions has increased the demand for intelligent systems that support safe and informed use of medicines. Medicine recognition and classification play a critical role in preventing medication errors, especially in scenarios where users rely on self‑medication or lack immediate access to healthcare professionals. This paper presents a comprehensive Medicine Recognition and Classification system that identifies medicines using visual and descriptive attributes and categorizes them based on therapeutic purpose. The proposed system adopts a web‑based architecture integrated with artificial intelligence techniques to analyze medicine images, textual inputs, and symptom‑based information. By combining recognition, classification, and structured guidance within a single platform, the system aims to enhance medicine awareness, reduce misuse, and provide preliminary decision support while clearly maintaining medical safety boundaries. The results demonstrate that the system is effective, user‑friendly, and suitable for real‑world deployment as a supportive healthcare application. `

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-01-08
DOI
https://doi.org/10.5281/zenodo.18181877
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Medicine Recognition and Classification

Laxman Sanjay Patil, Sushantgouda Kempe, Sumit Malagoudanavar, Duradundi Rudragoudar et al.
Zenodo (CERN European Organization for Nuclear Research)
Machine Learning in Healthcare
article

Medicine Recognition and Classification

Laxman Sanjay Patil, Sushantgouda Kempe, Sumit Malagoudanavar, Duradundi Rudragoudar, Dr. Yasmeen Shaikh
article en

Abstract

The rapid growth of digital healthcare solutions has increased the demand for intelligent systems that support safe and informed use of medicines. Medicine recognition and classification play a critical role in preventing medication errors, especially in scenarios where users rely on self‑medication or lack immediate access to healthcare professionals. This paper presents a comprehensive Medicine Recognition and Classification system that identifies medicines using visual and descriptive attributes and categorizes them based on therapeutic purpose. The proposed system adopts a web‑based architecture integrated with artificial intelligence techniques to analyze medicine images, textual inputs, and symptom‑based information. By combining recognition, classification, and structured guidance within a single platform, the system aims to enhance medicine awareness, reduce misuse, and provide preliminary decision support while clearly maintaining medical safety boundaries. The results demonstrate that the system is effective, user‑friendly, and suitable for real‑world deployment as a supportive healthcare application. `

Zenodo (CERN European Organization for Nuclear Research)
Openalex Percentile: Top 93%
Machine Learning in Healthcare
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