Published Paper Details:

Counterfeit medicine poses a significant threat to public health, with the potential to cause harm and undermine trust in healthcare systems. Traditional methods of detecting counterfeit drugs often fall short due to the increasing sophistication of counterfeiters. This research paper is an approach to tackle the issue by employing deep learning algorithms for image classification. It leverages the power of Deep Learning (DL) algorithms to automatically learn relevant features from images of pharmaceutical products and accurately distinguish between genuine and counterfeit drugs. The experimental results demonstrate the effectiveness of the DL model in achieving high accuracy and robustness in identifying counterfeit drugs. It not only advances pharmaceutical security measures but also showcases the potential of DL functions in image classification for safeguarding public health and safety. By comparing with the DL algorithms, it specifies the accuracy of the counterfeit drugs.