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

Ji Yeon Kim1, Du Hyung Choi1

  • 1College of Pharmacy, Daegu Catholic University, Gyeongsan-si, Gyeongbuk 38430, Republic of Korea.

International Journal of Pharmaceutics
|May 8, 2025
PubMed
Summary

A novel integrated model using convolutional neural networks (CNNs) and gray-level co-occurrence matrix (GLCM) features effectively classifies and quantifies tablet sticking in pharmaceutical manufacturing. This advanced system ensures drug product quality and improves manufacturing efficiency.

Keywords:
Convolutional neural networkDeep learningImage analysisStickingSupport vector machinesVisual inspection

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Area of Science:

  • Pharmaceutical Manufacturing
  • Drug Product Quality Control
  • Computational Imaging

Background:

  • Tablet sticking is a critical issue impacting drug quality, manufacturing efficiency, and therapeutic efficacy.
  • Traditional methods like visual inspection and quality attribute testing may fail to detect subtle sticking problems.
  • Developing automated, sensitive detection methods for tablet sticking is essential for robust pharmaceutical production.

Purpose of the Study:

  • To develop and validate a novel integrated model for classifying and quantifying tablet sticking in pharmaceutical manufacturing.
  • To evaluate the performance of different convolutional neural network (CNN) architectures for tablet sticking classification.
  • To assess the capability of the integrated model in detecting mild sticking that might be missed by conventional quality control measures.

Main Methods:

  • An integrated model combining convolutional neural network (CNN) architectures (AlexNet, VGG 16, ResNet 50, GoogLeNet) with gray-level co-occurrence matrix (GLCM) features and a support vector machine was developed.
  • GoogLeNet demonstrated superior performance in classifying tablet sticking.
  • GLCM features were analyzed to quantify sticking severity, and an integrated classification-quantification model was validated on a rotary tablet press.

Main Results:

  • GoogLeNet achieved the highest classification accuracy (99.39%), precision (100.00%), recall (98.78%), and F1-score (99.38%).
  • The quantification model successfully identified and measured sticking regions using a sticking index, showing significant differences between sticking and non-sticking areas.
  • Validation demonstrated the model's ability to detect minimal and classified sticking levels with high repeatability, even when tablet quality attributes remained within acceptable limits.

Conclusions:

  • The proposed integrated CNN-GLCM-SVM model effectively classifies and quantifies tablet sticking, surpassing the limitations of visual inspection and standard quality attribute testing.
  • This advanced detection system can significantly improve pharmaceutical manufacturing efficiency and ensure consistent drug product quality by identifying mild sticking issues.
  • The model offers a robust solution for enhancing quality control in tablet manufacturing, ensuring patient safety and drug efficacy.