Abstract：
The aim of this study was to use deep learning based on a deep convolutional neural network (DCNN) for automated image classification of healthy optic discs (OD) and visible optic disc drusen (ODD) on fundus autofluorescence (FAF) and color fundus photography (CFP). In this study, a total of 400 FAF and CFP images of patients with ODD and healthy controls were used. A pre-trained multi-layer Deep Convolutional Neural Network (DCNN) was trained and validated independently on FAF and CFP images. Training and validation accuracy and cross-entropy were recorded. Both generated DCNN classifiers were tested with 40 FAF and CFP images (20 ODD and 20 controls). After the repetition of 1000 training cycles, the training accuracy was 100%, the validation accuracy was 92% (CFP) and 96% (FAF), respectively. The cross-entropy was 0.04 (CFP) and 0.15 (FAF). The sensitivity, specificity, and accuracy of the DCNN for classification of FAF images was 100%. For the DCNN used to identify ODD on color fundus photographs, sensitivity was 85%, specificity 100%, and accuracy 92.5%. Differentiation between healthy controls and ODD on CFP and FAF images was possible with high specificity and sensitivity using a deep learning approach.
摘要：
这项研究的目的是使用基于深度卷积神经网络（DCNN）的深度学习对眼底自发荧光（FAF）和彩色眼底照相（CFP）的健康视盘（OD）和可见视盘玻璃疣（ODD）进行自动图像分类。在这项研究中,共使用了ODD患者和健康对照组的400张FAF和CFP图像.在FAF和CFP图像上独立训练和验证预训练的多层深度卷积神经网络(DCNN)。记录训练和验证的准确性和交叉熵。用40个FAF和CFP图像(20个ODD和20个对照)测试两个生成的DCNN分类器。重复1000个训练周期后，训练准确率为100%,验证准确率为92%(CFP)和96%(FAF)，分别。交叉熵为0.04(CFP)和0.15(FAF)。敏感性,特异性，DCNN对FAF图像分类的准确率为100%。对于用于在彩色眼底照片上识别ODD的DCNN，灵敏度为85%,特异性100%，准确率为92.5%。使用深度学习方法，可以在CFP和FAF图像上区分健康对照和ODD，具有高特异性和敏感性。
