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Deep Neural Networks for Image-Based Dietary Assessment
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Development of fine-grained pill identification algorithm using deep convolutional network.

Yuen Fei Wong1, Hoi Ting Ng2, Kit Yee Leung3

  • 1Department of Pharmacy, Faculty of Medicine, University of Malaya, Malaysia.

Journal of Biomedical Informatics
|September 20, 2017
PubMed
Summary

A Deep Convolutional Network (DCN) model accurately identifies oral pills from mobile phone images, outperforming traditional methods. This deep learning approach offers a robust solution for automatic pill verification, enhancing patient safety.

Keywords:
AutomaticCapsuleDeep learningErrorTablet

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Area of Science:

  • Pharmaceutical Sciences
  • Computer Science
  • Artificial Intelligence

Background:

  • Oral pills are a common and stable dosage form, but misidentification poses a risk in healthcare.
  • Existing pill identification methods lack sufficient accuracy, especially in unconstrained environments.

Purpose of the Study:

  • To develop and evaluate a Deep Convolutional Network (DCN) for automatic pill identification and verification.
  • To create a system that surpasses current methods in accuracy and reliability.

Main Methods:

  • A DCN model was trained using pill images captured via mobile phones under varied conditions.
  • The DCN model's performance was benchmarked against two traditional hand-crafted feature-based methods.

Main Results:

  • The DCN model achieved a Top-1 accuracy of 95.35%, significantly outperforming baseline methods (89.00% and 70.65%).
  • High accuracy was maintained even with suboptimal image quality, including various angles and illumination levels.
  • Top-5 and Top-10 accuracy rates for the DCN were 98.75% and 99.55%, respectively.

Conclusions:

  • Deep Convolutional Networks demonstrate superior performance for pill identification and verification tasks.
  • The DCN model shows significant potential for real-world applications in improving medication safety and accuracy.