Left-vs-Right Eye Discrimination Based on Convolutional Neural Network
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摘要: 利用一个深度卷积神经网络提出并验证了一种能自动判别左右眼的新方法.根据左眼和右眼图像的特征差异性，所设计的网络能够自动估计网络的所有参数.在Alexnet网络的基础之上进行修改，设计的卷积神经网络由4个卷积池化层和2个全连接层组成，其次是作为最后一层的分类器.基于针对42541张眼底图像的实验结果，所提出的网络的训练精度约为100%，测试精度高达99%.此外，所提出的网络具有高度的鲁棒性，它可以成功地识别大量具有多变性的眼底图像.据所知，这是第1个基于深度学习用于左右眼识别的高精准度网络.Abstract: In this paper, a new method to automatically discriminate the left and right eyes is proposed and validated utilizing a deep convolutional neural network. All parameters of the designed network are automatically estimated based on the characteristic differences between the left and right eye images. On the basis of the Alexnet network, the convolutional neural network designed in this paper consists of four convolutional layers and two fully connected layers, followed by a classifier serving as its last layer. According to our experimental results on a total of 42541 fundus images, the training accuracy of our network is about 100%, and the testing accuracy is as high as 99%. In addition, the proposed network is highly robust given that it successfully works for a large amount of fundus images with great variability. As far as we know, this is the first deep learning based network for left-vs-right eye discrimination that exhibits very high accuracy and precision.
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