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Most eye diseases, if not diagnosed, can result in severe vision impairments. The OCT scan images of the retina give a clear perception; hence, it is useful in diagnosing specific eye diseases. In this paper, the state-of-the-art deep learning technique is adopted with three CNN architectures: VGG16, InceptionV3, and InceptionResNetV2 for classifying diseases from OCT images. In this regard, ensemble methods tend to supplement the strength of each model and refine the classification accuracy. The high accuracy of 98.86% in the identification of ocular diseases has been improved with a marked improvement in models adapted to the OCT dataset, reusing existing knowledge.

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Deep Learning and OCT Imaging: A Novel Ensemble Approach for Eye Disease Diagnosis

  • 会议论文

  • 发表日期：

    2026年

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  • Dodda Abhiram,
  • R. Aruna Flarence,
  • K. Anuradha,
  • V. Srilakshmi

摘要

Most eye diseases, if not diagnosed, can result in severe vision impairments. The OCT scan images of the retina give a clear perception; hence, it is useful in diagnosing specific eye diseases. In this paper, the state-of-the-art deep learning technique is adopted with three CNN architectures: VGG16, InceptionV3, and InceptionResNetV2 for classifying diseases from OCT images. In this regard, ensemble methods tend to supplement the strength of each model and refine the classification accuracy. The high accuracy of 98.86% in the identification of ocular diseases has been improved with a marked improvement in models adapted to the OCT dataset, reusing existing knowledge.