Detection and Identification of Pills using Machine Learning

CH.Vasundhara1, Gundarapu Varasai Mani Vaasavi2

1 Assistant Professor, 2 MCA Final Semester, Master of Computer Applications,

Sanketika Vidya Parishad Engineering College, Vishakhapatnam,

Andhra Pradesh, India

vasundharacse@svpec.edu.in , vaasavigundarapu@gmail.com

ABSTRACT:

The increasing variety of pharmaceutical pills available in the healthcare industry has made accurate pill identification a critical task for ensuring patient safety and reducing medication errors. This project presents a machine learning-based system for the detection and identification of pills using image processing and deep learning techniques. The proposed system analyzes images of pills by extracting key visual features such as shape, color, size, and imprint patterns. A convolutional neural network (CNN) is trained on a labeled dataset of pill images to accurately classify and identify different medications. Image preprocessing techniques, including resizing, normalization, and background removal, improve the quality of input data and enhance model performance. The trained model can identify pills in real time, providing reliable results with high accuracy. This approach can assist healthcare professionals, pharmacists, and patients in verifying medications, minimizing prescription errors, and preventing the misuse of drugs. The proposed system demonstrates the effectiveness of machine learning in pharmaceutical image recognition and offers a scalable solution for automated pill identification in healthcare applications.

Keywords: Machine Learning, Deep Learning, Convolutional Neural Network (CNN), Image Processing, Pill Detection, Pill Identification, Healthcare, Computer Vision.