Work

Advanced Pharmaceutical Recognition System Based on Deep Learning for Mobile Medication Identification

Year: 2025

Type: article

Abstract: Medication misidentification poses a significant risk to patient safety, particularly for elderly individuals managing complex prescriptions. To address this, we developed a deep learning-based system for real-time medication recognition on mobile devices. Through a comparative analysis of convolutional neural networks, ResNet101 was selected for its superior performance, achieving 98.51% accuracy on a dataset from the Korea Pharmaceutical Information Center. The system employs advanced preprocessing techniques, including image augmentation and normalization, to ensure robustness across diverse conditions. Heatmap-based visualizations enhance model interpretability, fostering trust in their decisions. Deployed as a user-friendly mobile application, the system prioritizes accessibility for elderly users, offering a practical solution to reduce medication errors. This research demonstrates the potential of AI-driven mobile health applications to improve pharmaceutical safety and patient outcomes.

Source: Applied Sciences

DOI: 10.3390/app15105644

Authors: Seongheon Kim, Minsu Chae, Jeungmin Lee, Hwamin Lee

Institution: Korea University

Language: English

Topic: Advanced Neural Network Applications

Subfield: Computer Vision and Pattern Recognition

Field: Computer Science

Domain: Physical Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $2,896