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<p>Ocular diseases remain a major cause of vision impairment globally, making early and accurate diagnosis essential. This study presents a novel diagnostic model for identifying seven common ocular conditions age-related macular degeneration, glaucoma, hypertension, diabetic retinopathy, myopia, cataracts, and other pathologies using clinical fundus images. To improve image quality, Anisotropic Diffusion Filtering and Wavelet Transform are applied for hue and contrast enhancement. Data imbalance is addressed through targeted augmentation techniques. The core of the model is a hybrid Quantum Convolutional Neural Network (QCNN), which integrates quantum convolutional pooling into a classical CNN architecture to boost feature extraction and classification. Evaluated on the OIA-ODIR dataset, the proposed model outperformed benchmarks such as Fundus-DeepNet, Inception-v4, VGG16 with SGD, and ResNet-101. It achieved 94% classification accuracy, along with substantial gains in precision, recall, and F1-score. These results confirm the model’s effectiveness and its potential for supporting early, multi-disease ocular diagnosis in clinical settings.</p>

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Identification of multiple ocular diseases using a hybrid quantum convolutional neural network with fundus images

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月31日

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  • Ans Ibrahim Mahameed Alqassab,
  • M.-Á. Luque-Nieto,
  • Mazin Abed Mohammed

摘要

Ocular diseases remain a major cause of vision impairment globally, making early and accurate diagnosis essential. This study presents a novel diagnostic model for identifying seven common ocular conditions age-related macular degeneration, glaucoma, hypertension, diabetic retinopathy, myopia, cataracts, and other pathologies using clinical fundus images. To improve image quality, Anisotropic Diffusion Filtering and Wavelet Transform are applied for hue and contrast enhancement. Data imbalance is addressed through targeted augmentation techniques. The core of the model is a hybrid Quantum Convolutional Neural Network (QCNN), which integrates quantum convolutional pooling into a classical CNN architecture to boost feature extraction and classification. Evaluated on the OIA-ODIR dataset, the proposed model outperformed benchmarks such as Fundus-DeepNet, Inception-v4, VGG16 with SGD, and ResNet-101. It achieved 94% classification accuracy, along with substantial gains in precision, recall, and F1-score. These results confirm the model’s effectiveness and its potential for supporting early, multi-disease ocular diagnosis in clinical settings.