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Updated: Jun 1, 2025

Deep Neural Networks for Image-Based Dietary Assessment

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Deep learning-based defect detection in film-coated tablets using a convolutional neural network.

Kabir A Pathak¹, Prapti Kafle¹, Ajit Vikram²

¹Pharmaceutical Sciences and Clinical Supply, Merck & Co., Inc., Rahway, NJ, USA.

International Journal of Pharmaceutics

|January 20, 2025

Summary

This summary is machine-generated.

This study introduces a machine learning approach for automated film-coated tablet defect detection. The developed convolutional neural network (CNN) achieves 99.7% accuracy, significantly improving upon traditional methods for pharmaceutical quality control.

Keywords:

Convolutional neural network Defects Film-Coated tablets Image Analysis Machine learning

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Area of Science:

Pharmaceutical Manufacturing
Computer Vision
Machine Learning

Background:

Traditional visual inspection of film-coated tablets for defects is subjective and inefficient.
Automated defect detection is crucial for ensuring pharmaceutical product quality and manufacturing efficiency.

Purpose of the Study:

To develop and evaluate a novel machine learning-based approach for objective and efficient film-coated tablet defect detection.
To compare the performance of a convolutional neural network (CNN) against a static rule-based method.

Main Methods:

Manually induced defects (chipping, breaking, color non-uniformity, speckling) in film-coated tablets.
Image acquisition using a 3-D printed tray and a unique segmentation approach.
Training a CNN on 25,200 augmented images for multi-class defect classification.

Main Results:

The CNN model achieved 99.7% accuracy in detecting defects in film-coated tablets.
The CNN significantly outperformed a static rule-based method, which showed high error rates in dimensional analysis.
The model demonstrated robustness through data augmentation techniques.

Conclusions:

The proposed CNN-based image analysis offers a standardized, objective, and efficient method for pharmaceutical tablet defect detection.
This approach provides a valuable tool for enhancing product quality and accelerating pharmaceutical development.