The proposed deep learning framework for glaucoma classification addresses critical challenges of limited data and computational costs. Employing data augmentation and normalization techniques, the three-stage model, utilizing InceptionV3 and ResNet50, achieves high training (99.3% - 99.8%) and testing accuracy (91.6% - 92.12%) on a dataset comprising 16,328 images from fused public datasets. This outperforms existing automated models. The approach leverages transfer learning and convolutional neural networks, showcasing its potential for accurate and timely glaucoma diagnosis. However, ongoing validation on diverse datasets and ethical considerations regarding fairness and transparency in medical applications remain essential. The model's reliability suggests its promising role in aiding early glaucoma detection, potentially averting irreversible vision impairment.

中文翻译：

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使用视网膜图像进行基于深度学习的青光眼网络分类 (GNC)

所提出的青光眼分类深度学习框架解决了有限数据和计算成本的关键挑战。该三阶段模型采用数据增强和归一化技术，利用 InceptionV3 和 ResNet50，在包含来自融合公共数据集的 16,328 张图像的数据集上实现了较高的训练 (99.3% - 99.8%) 和测试准确性 (91.6% - 92.12%)。这优于现有的自动化模型。该方法利用迁移学习和卷积神经网络，展示了其准确及时诊断青光眼的潜力。然而，对不同数据集的持续验证以及有关医疗应用公平性和透明度的道德考虑仍然至关重要。该模型的可靠性表明它在帮助早期青光眼检测方面发挥着巨大的作用，有可能避免不可逆转的视力损伤。