Automated Fake vs Real Medicine Classification Using Deep Learning and Web-Based Deployment

The circulation of counterfeit medicines poses a serious threat to public health and safety. This project presents an automated system for classifying real and fake medicines using deep learning-based image analysis. The proposed approach utilizes Convolutional Neural Networks (CNN) along with advanced architectures such as ResNet and DenseNet to extract meaningful features from medicine images. The models are trained and evaluated on labeled datasets containing images of genuine and counterfeit medicine packaging. Based on the learned visual patterns, the system accurately classifies the input image as real or fake. The best-performing model is deployed through a web-based application, allowing users to upload medicine images and receive instant classification results. This solution provides a fast, reliable, and user-friendly approach for counterfeit medicine detection and supports efforts to enhance pharmaceutical safety.

Keywords:
Fake Medicine Detection, Deep Learning, Convolutional Neural Network (CNN), ResNet, DenseNet, Image Classification, Kaggle dataset, Web-Based Deployment.