Deep learning-based image classification and quantification models for tablet sticking

Sticking can significantly affect drug product quality, manufacturing efficiency, and therapeutic efficacy in pharmaceutical tablet manufacturing. This study presents a novel integrated model with a convolutional neural network (CNN) and gray-level co-occurrence matrix (GLCM) based features combined with a support vector machine to classify and quantify tablet sticking. The classification model was developed and evaluated using CNN architectures, including AlexNet, VGG 16, ResNet 50, and GoogLeNet. GoogLeNet showed the best performance in terms of accuracy (99.39 %), precision (100.00 %), recall (98.78 %), F1-score (99.38 %), and computational efficiency. GLCM features such as energy, homogeneity, contrast, and correlation were analyzed to develop an optimal quantification model, revealing a significant difference between the sticking and non-sticking regions. Based on these differences, the sticking regions were detected and quantified using a sticking index. To validate the final model, which integrated the classification and quantification models, 10 batches of tablets were produced using a rotary tablet press. The validation confirmed high measurement repeatability with minimal and classified sticking levels. Tablet quality attributes such as assay, content uniformity, and weight were evaluated. Despite the occurrence of sticking, the tablet quality attributes met their criteria. These results suggest that measuring tablet quality attributes and visual inspection may not be sufficient to detect mild sticking. However, the integrated model proposed in this study could detect mild sticking, even if the tablet quality attributes remained within the acceptable criteria. This study demonstrated that the proposed integrated model could improve pharmaceutical manufacturing efficiency, ensure consistent drug product quality, and overcome visual inspection limitations.