Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool
Automatic detection of diabetic retinopathy using an artificial neural network: A screening tool
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DOI：
10.1136/bjo.80.11.940
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发表时间：
1996-11-01
影响因子：
4.1
通讯作者：
Elliott, AT
中科院分区：
文献类型：
--
作者：
Gardner, GG;Keating, D;Elliott, AT
目的：确定神经网络是否可以检测眼底图像中的糖尿病特征，并将神经网络与眼科医生筛选一组眼底图像进行比较。方法：从眼底照相机采集147张糖尿病患者和32张正常人的图像，存储在计算机上，并使用反向传播神经网络进行分析。该网络被训练来识别视网膜图像中的特征。评估了数字滤波技术和不同网络变量的影响。然后随机抽取200张糖尿病和101张正常图像，用来评估该网络在检测糖尿病视网膜病变方面的性能。结果：血管、渗出液和出血的检出率分别为91.7%、93.1%和73.8%。与眼科医生的结果相比，该网络对糖尿病视网膜病变的检测灵敏度为88.4%，特异性为83.5%。结论：血管、渗出物和出血的检测是可能的，成功率取决于预处理和训练中使用的图像数量。与眼科医生相比，该网络对糖尿病视网膜病变的检测准确率较高。该系统可用于辅助糖尿病患者视网膜病变的筛查。
Aims - To determine if neural networks can detect diabetic features in fundus images and compare the network against an ophthalmologist screening a set of fundus images.Methods - 147 diabetic and 32 normal images were captured from a fundus camera, stored on computer, and analysed using a back propagation neural network. The network was trained to recognise features in the retinal image. The effects of digital filtering techniques and different network variables were assessed. 200 diabetic and 101 normal images were then randomised and used to evaluate the network's performance for the detection of diabetic retinopathy against an ophthalmologist.Results - Detection rates for the recognition of vessels, exudates, and haemorrhages were 91.7%, 93.1%, and 73.8% respectively. When compared with the results of the ophthalmologist, the network achieved a sensitivity of 88.4% and a specificity of 83.5% for the detection of diabetic retinopathy.Conclusions - Detection of vessels, exudates, and haemorrhages was possible, with success rates dependent upon pre-processing and the number of images used in training. When compared with the ophthalmologist, the network achieved good accuracy for the detection of diabetic retinopathy. The system could be used as an aid to the screening of diabetic patients for retinopathy.