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Pyramidal deep neural network for classification of retinal OCT images

Almasganj, M ; Sharif University of Technology | 2023

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  1. Type of Document: Article
  2. DOI: 10.1109/ICBME61513.2023.10488597
  3. Publisher: Institute of Electrical and Electronics Engineers Inc , 2023
  4. Abstract:
  5. Retinal optical coherence tomography (OCT) images are widely used to diagnose and grade macular diseases, such as age-related macular degeneration (AMD). However, manual interpretation of OCT images is time-consuming and subjective. Therefore, automated and accurate classification of OCT images is essential for assisting ophthalmologists in clinical decision-making. This paper proposes a pyramidal deep neural network that can diagnose normal and two types of AMD (dry and wet) in OCT images. Our network leverages features from different scales of a pre-trained convolutional neural network (CNN) and integrates them with two advanced versions of feature pyramid networks: bidirectional feature pyramid network (BiFPN) and path aggregation network (PANet). We evaluate our network on the NEH dataset and compare it with its predecessor. Our results show that our BiFPN-VGG16 and PAN-VGG16 models achieve accuracies of 94.S% and 95.0%, respectively, which are 2.8 to 3% higher than the previous models. Our approach demonstrates the potential of multi-scale feature networks for OCT image classification and can serve as an auxiliary diagnostic tool for ophthalmologists. © 2023 IEEE
  6. Keywords:
  7. Age-related macular degeneration (AMD) ; Convolutional neural networks ; Image classification ; Multi-scale feature networks ; Optical coherence tomography (OCT)
  8. Source: 2023 30th National and 8th International Iranian Conference on Biomedical Engineering, ICBME 2023 ; 2023 , Pages 381-385 ; 979-835035973-2 (ISBN)
  9. URL: https://ieeexplore.ieee.org/document/10488597