We propose a new method for training convolutional neural networks (CNNs) and use it to classify glaucoma from fundus images. This method integrates reinforcement learning along with supervised learning and uses it for transfer learning. The training method uses hill climbing techniques via two different climber types, namely, “random movement” and “random detection,” integrated with a supervised learning model through a stochastic gradient descent with momentum model. The model was trained and tested using the Drishti-GS and RIM-ONE-r2 datasets having glaucomatous and normal fundus images. The performance for prediction was tested by transfer learning on five CNN architectures, namely, GoogLeNet, DenseNet-201, NASNet, VGG-19, and Inception-Resnet v2. A five-fold classification was used for evaluating the performance, and high sensitivities while maintaining high accuracies were achieved. Of the models tested, the DenseNet-201 architecture performed the best in terms of sensitivity and area under the curve. This method of training allows transfer learning on small datasets and can be applied for tele-ophthalmology applications including training with local datasets.

中文翻译：

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青光眼眼底图像的快速分类


我们提出了一种训练卷积神经网络（CNN）的新方法，并用它来对眼底图像中的青光眼进行分类。该方法将强化学习与监督学习相结合，并将其用于迁移学习。该训练方法通过两种不同的登山者类型（即“随机运动”和“随机检测”）使用爬山技术，并通过动量模型的随机梯度下降与监督学习模型集成。该模型使用具有青光眼和正常眼底图像的 Drishti-GS 和 RIM-ONE-r2 数据集进行训练和测试。通过在五种 CNN 架构（即 GoogLeNet、DenseNet-201、NASNet、VGG-19 和 Inception-Resnet v2）上进行迁移学习来测试预测性能。使用五重分类来评估性能，在保持高精度的同时实现了高灵敏度。在测试的模型中，DenseNet-201 架构在灵敏度和曲线下面积方面表现最好。这种训练方法允许在小型数据集上进行迁移学习，并且可应用于远程眼科应用，包括使用本地数据集进行训练。