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Classification of optical coherence tomography images using a capsule network.

Takumasa Tsuji1, Yuta Hirose1, Kohei Fujimori1

  • 1Graduate School of Medical and Care Technology, Teikyo University, Tokyo, Japan.

BMC Ophthalmology
|March 21, 2020
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Summary

Capsule networks significantly improve optical coherence tomography (OCT) image classification accuracy. This deep learning approach enhances diagnostic capabilities for conditions like choroidal neovascularization (CNV) and diabetic macular edema (DME).

Keywords:
Capsule networkChoroidal neovascularizationDeep learningDiabetic macular edemaDrusenOptical coherence tomography

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Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Classical Convolutional Neural Networks (CNNs) show high accuracy in Optical Coherence Tomography (OCT) image classification.
  • CNNs often suppress crucial positional information during pooling layers.
  • Capsule networks offer a potential solution by preserving spatial hierarchies within images.

Purpose of the Study:

  • To investigate the efficacy of capsule networks in OCT image classification.
  • To improve diagnostic accuracy by leveraging the positional learning capabilities of capsule networks.
  • To overcome the limitations of CNNs in capturing spatial relationships in OCT data.

Main Methods:

  • A large dataset of 83,484 OCT images was curated for training and 1000 for testing.
  • The dataset included normal images and those with choroidal neovascularization (CNV), diabetic macular edema (DME), and drusen.
  • A novel model based on capsule networks was developed and trained on the OCT dataset.

Main Results:

  • The proposed capsule network model achieved a classification accuracy of 99.6%.
  • This represents a 3.2 percentage point improvement over existing methods.
  • The model demonstrated high performance across all tested OCT image categories.

Conclusions:

  • Capsule networks offer a superior approach for OCT image classification compared to traditional CNNs.
  • The developed method achieves state-of-the-art accuracy for diagnosing conditions like CNV, DME, and drusen.
  • This advancement holds promise for enhancing computer-aided diagnosis in ophthalmology.