Leveraging Convolutional Neural Networks for Improved Drug Identification

The use of Convolutional Neural Network (CNN) in drug identification has become a crucial area of research in the pharmaceutical industry. Medicine business is among most prior sectors and people think of the uppermost level of care or facilities irrespective of the cost. Medicine key information includes imprint, shape, color, and size. Imprint is indented or written on each medicine in the form of symbols, digits, alphabets, or a combination of these. What's the deal with identifying medicine? As various legal or illegal medicine is around the market it's difficult to differentiate between original and fake. Forget about the normal person it's also difficult for an experienced pharmacist to differentiate the drugs. In the proposed work, CNN is used to identify the type of medicine based on the imprint. The model was trained on Kaggle dataset of drug images, and the performance was evaluated using accuracy. The results showed that the proposed CNN model outperformed traditional image recognition methods and achieved a high accuracy in drug identification. This study demonstrates the potential of CNNs in automating the drug identification process, which could significantly improve the efficiency and accuracy of the pharmaceutical industry. The findings of this study have important implications for the development of efficient and reliable drug identification systems in the future.