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In recent years, there has been a growing focus on improving the diagnosis of diabetic retinopathy (DR) through machine learning and deep learning technologies. Researchers have explored various methods, including the use of high-definition medical imaging and AI-driven algorithms like convolutional neural networks (CNNs) and generative adversarial networks (GANs). Among these tools, CNNs have become the preferred choice due to their exceptional classification accuracy and efficiency. While CNNs offer relatively high accuracy, there is potential for further improvement by incorporating hybrid models that combine various machine learning and deep learning techniques. In this paper, an ensemble learning approach is proposed to enhance the early detection and management of DR with greater accuracy. The model is tested on the APTOS, EyePACS, and Messidor datasets, demonstrating superior validation accuracy in the range of 96%–99% compared to previous models. To ensure a domain-generalized approach, the model is trained on one dataset and tested on another, making it a valuable tool for the early detection and treatment of DR and improving the overall quality of care for affected individuals.

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Enhancing Eye Disease Diagnosis with Deep Learning and Synthetic Data Augmentation

  • 会议论文

  • 发表日期：

    2026年

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  • Saideep Kilaru,
  • Kothamasu Jayachandra,
  • Tanishka Yagneshwar,
  • Suchi Kumari

摘要

In recent years, there has been a growing focus on improving the diagnosis of diabetic retinopathy (DR) through machine learning and deep learning technologies. Researchers have explored various methods, including the use of high-definition medical imaging and AI-driven algorithms like convolutional neural networks (CNNs) and generative adversarial networks (GANs). Among these tools, CNNs have become the preferred choice due to their exceptional classification accuracy and efficiency. While CNNs offer relatively high accuracy, there is potential for further improvement by incorporating hybrid models that combine various machine learning and deep learning techniques. In this paper, an ensemble learning approach is proposed to enhance the early detection and management of DR with greater accuracy. The model is tested on the APTOS, EyePACS, and Messidor datasets, demonstrating superior validation accuracy in the range of 96%–99% compared to previous models. To ensure a domain-generalized approach, the model is trained on one dataset and tested on another, making it a valuable tool for the early detection and treatment of DR and improving the overall quality of care for affected individuals.