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@article{177413,
        author = {Nonika Nissy.Kariketi and Mr. Suresh Tiruvalluru},
        title = {MobileNet architecture -Based Pill Detection System for Accurate Drug Identification in Healthcare},
        journal = {International Journal of Innovative Research in Technology},
        year = {2025},
        volume = {11},
        number = {12},
        pages = {544-548},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=177413},
        abstract = {Accurate pill identification is vital for patient safety, especially in environments like hospitals, pharmacies, and eldercare, where medication errors can be life-threatening. Manual verification methods are prone to error, particularly with visually similar pills. This paper presents a lightweight, efficient, and accurate pill detection and classification system using the MobileNet architecture, optimized for deployment on mobile and edge devices. Leveraging transfer learning and a curated pill image dataset, the system is capable of identifying pills based on shape, color, imprint, and size. Experimental results show that the MobileNet-based model achieves high classification accuracy with minimal latency, making it ideal for real-time, on-device healthcare applications. The system improves drug safety and supports healthcare professionals in minimizing medication errors.},
        keywords = {Medication Identification, Pill Detection, MobileNet Architecture, Drug Safety, Healthcare Automation.},
        month = {May},
        }