Cataract is one of the most common eye disorders that causes vision distortion. Accurate and timely detection of cataracts is the best way to control the risk and avoid blindness. Recently, artificial intelligence-based cataract detection systems have been received research attention. In this paper, a novel deep neural network, namely CataractNet, is proposed for automatic cataract detection in fundus images. The loss and activation functions are tuned to train the network with small kernels, fewer training parameters, and layers. Thus, the computational cost and average running time of CataractNet are significantly reduced compared to other pre-trained Convolutional Neural Network (CNN) models. The proposed network is optimized with the Adam optimizer. A total of 1130 cataract and non-cataract fundus images are collected and augmented to 4746 images to train the model. For avoiding the over-fitting problem, the dataset is extended through augmentation before model training. Experimental results prove that the proposed method outperforms the state-of-the-art cataract detection approaches with an average accuracy of 99.13%.

中文翻译：

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CataractNet：使用深度学习进行眼底图像的自动白内障检测系统


白内障是导致视力扭曲的最常见眼部疾病之一。准确及时地发现白内障是控制风险、避免失明的最佳方法。近年来，基于人工智能的白内障检测系统受到了研究关注。在本文中，提出了一种新颖的深度神经网络，即 CataractNet，用于眼底图像中的自动白内障检测。损失函数和激活函数经过调整，可以使用较小的内核、较少的训练参数和层来训练网络。因此，与其他预训练的卷积神经网络（CNN）模型相比，CataractNet 的计算成本和平均运行时间显着降低。所提出的网络使用 Adam 优化器进行优化。总共收集了 1130 张白内障和非白内障眼底图像，并将其增强至 4746 张图像来训练模型。为了避免过拟合问题，在模型训练之前通过扩充来扩展数据集。实验结果证明，该方法优于最先进的白内障检测方法，平均准确率为 99.13%。