Adulterated Medicine Detection System

The global threat of falsified and substandard medicines undermines public health, leading to ineffective treatments, heightened drug resistance, and serious patient harm.Existing verification methods-including lab tests and chemical analysis-are typically too slow, expensive, and immobile for rapid field deployment.In response, this work develops an image-analysis system powered by Convolutional Neural Networks, designed to automatically authenticate pharmaceuticals and flag counterfeit products.The designed system employs deep learning to examine visual characteristics of drug forms-including tablets, capsules, labels, and packaging.Users can submit an image via the system's interface, where a pre-trained CNN model analyses it by identifying distinctive attributes such as color, texture, shape, imprint markings, and packaging design.Using these extracted features, the model then provides a predictive assessment regarding the authenticity of the medicine in question. VI. CONCLUSIONThis work details a deep learning-based system for detecting adulterated pharmaceuticals, utilizing Convolutional Neural Networks and transfer learning to accurately classify medicine images as genuine or counterfeit.The approach provides a robust alternative to traditional manual inspection by achieving superior speed and reliability.The model is deployed via a streamlined Flask web application, enabling real-time analysis with secure login, efficient image processing, and interpretable results.This accessible tool is designed to serve stakeholders across the supply chain, from regulators to end consumers, enhancing verification with confidence-scored predictions.VII.