4.6 Article

Detection of Diabetic Retinopathy Using Extracted 3D Features from OCT Images

期刊

SENSORS
卷 22, 期 20, 页码 -

出版社

MDPI
DOI: 10.3390/s22207833

关键词

diabetic retinopathy; neural networks; thickness; OCT; reflectivity; classification

资金

  1. Princess Nourah bint Abdulrahman University Researchers Supporting Project [PNURSP2022R40]
  2. Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia

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This study presents a three-step system for detecting diabetic retinopathy using optical coherence tomography (OCT). Experimental results show that the proposed system outperforms related methods and achieves a high accuracy rate.
Diabetic retinopathy (DR) is a major health problem that can lead to vision loss if not treated early. In this study, a three-step system for DR detection utilizing optical coherence tomography (OCT) is presented. First, the proposed system segments the retinal layers from the input OCT images. Second, 3D features are extracted from each retinal layer that include the first-order reflectivity and the 3D thickness of the individual OCT layers. Finally, backpropagation neural networks are used to classify OCT images. Experimental studies on 188 cases confirm the advantages of the proposed system over related methods, achieving an accuracy of 96.81%, using the leave-one-subject-out (LOSO) cross-validation. These outcomes show the potential of the suggested method for DR detection using OCT images.

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