A Deep Learning-Based Model for Automated Quality Control in the Pharmaceutical Industry

For highly sensitive products such as pharmaceuticals, quality is a decisive factor in ensuring the therapeutic benefit that consumers expect and not jeopardizing consumers' health. So far, the quality control of pharmaceuticals is largely performed manually by qualified individuals. However, this is a time-consuming, repetitive, and error-prone process subject to natural performance fluctuations. To contribute to addressing this issue, we present an automated quality control approach for pharmaceutical capsules using a transfer learning-based convolutional neural network with a balanced accuracy of 97.27%, outperforming all current benchmarks. To increase trust in the model predictions, we incorporated two explainable artificial intelligence (XAI) methods into our approach.