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This paper presents a novel approach for classifying OCT images to diagnose retinal diseases using the MobileNetV2 neural network. The proposed model achieves competitive results compared to the state-of-the-art, even using a significantly smaller model size. The proposed architecture is characterized by its optimized size, making it suitable for deployment on mobile devices and other resource-constrained environments. Overall, the results demonstrate the potential of the proposed method for efficient and accurate retinal disease diagnosis using OCT images.

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Automatic Disease Detection in OCT Images Using a Reduced-Size Deep Learning Model

  • 会议论文

  • 发表日期：

    2025年

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  • Vitor do Nascimento Ramos,
  • I. A. D. Vieira,
  • R. V. Andreão,
  • J. P. C. Cancellieri,
  • D. F. V. Valbon,
  • G. T. Zago

摘要

This paper presents a novel approach for classifying OCT images to diagnose retinal diseases using the MobileNetV2 neural network. The proposed model achieves competitive results compared to the state-of-the-art, even using a significantly smaller model size. The proposed architecture is characterized by its optimized size, making it suitable for deployment on mobile devices and other resource-constrained environments. Overall, the results demonstrate the potential of the proposed method for efficient and accurate retinal disease diagnosis using OCT images.