Smart Pill Detection Using Machine Learning Models
DOI:
https://doi.org/10.48001/978-81-966500-0-1-7Keywords:
Pill Identification, Deep Learning, Drug Errors, Manual Processes, Healthcare TechnologyAbstract
One of the most significant responsibilities of pharmaceutical safety is pill identification. The rapid advancement of technology has produced fresh chances to improve medication compliance, patient safety, and healthcare delivery. This is especially true in the healthcare industry. Pharmacies, including pills, tablets, and capsules, must be identified in order to ensure patient safety and the delivery of healthcare. In the past, this initiatives has primarily depended on manual processes and human judgement, which can be time-consuming and error-prone. Since drug errors can occur and can cause patient difficulties, proper prescription drafting is crucial for patient safety. These errors are mostly caused by label damage, inconsistencies in the way medications are taken, and other problems. This study looks at the use of deep learning and machine learning.
Downloads
References
Fung, E. Y., Leung, B., Hamilton, D., & Hope, J. (2009). Do automated dispensing machines improve patient safety? Canadian Journal of Hospital Pharmacy, 62(6), 516–519. https://doi.org/10.4212/cjhp.v62i6.852
Gordon, J. O., Hadsall, R. S., & Schommer, J. C. (2005). Automated medication-dispensing system in two hospital emergency departments. American Journal of Health-System Pharmacy, 62(18), 1917–1923. https://doi.org/10.2146/ajhp040481
Hartl, A. (2010). Computer-Vision based Pharmaceutical Pill Recognition on Mobile Phones. The 14th Central European Seminar on Computer Graphics (CESCG) (non-peer reviewed), 51–58. http://www.cescg.org/CESCG-2010/
Jara, A. J., Zamora, M. A., & Skarmeta, A. F. (2014). Drug identification and interaction checker based on IoT to minimize adverse drug reactions and improve drug compliance. Personal and Ubiquitous Computing, 18(1), 5–17. https://doi.org/10.1007/s00779-012-0622-2
Konda, A., Xin, L. C., Takadera, M., Okoshi, Y., & Tariumi, K. (1990). Evaluation of pilling by computer image analysis. Journal of the Textile Machinery Society of Japan. English Edition, 36(3). https://doi.org/10.4188/jte1955.36.96
Ramya, S., Suchitra, J., & Nadesh, R. K. (2013). Detection of broken pharmaceutical drugs using enhanced feature extraction technique. International Journal of Engineering and Technology, 5(2), 1407–1411.
Rani, G. E., Reddy, A. T. V., Vardhan, V. K., Harsha, A. S. S., & Sakthimohan, M(2020). Machine learning based cibil verification system. Proceedings of the 3rd International Conference on Smart Systems and Inventive Technology, ICSSIT 2020, 780–782. https://doi.org/10.1109/ICSSIT48917.2020.9214195
