Faculty of Electrical Engineering and Information Technology, Slovak University of Technology, Ilkovičova 3, 812 19, Bratislava, Slovakia.
Sci Rep. 2021 Aug 6;11(1):16045. doi: 10.1038/s41598-021-95519-0.
Hard exudates are one of the main clinical findings in the retinal images of patients with diabetic retinopathy. Detecting them early significantly impacts the treatment of underlying diseases; therefore, there is a need for automated systems with high reliability. We propose a novel method for identifying and localising hard exudates in retinal images. To achieve fast image pre-scanning, a support vector machine (SVM) classifier was combined with a faster region-based convolutional neural network (faster R-CNN) object detector for the localisation of exudates. Rapid pre-scanning filtered out exudate-free samples using a feature vector extracted from the pre-trained ResNet-50 network. Subsequently, the remaining samples were processed using a faster R-CNN detector for detailed analysis. When evaluating all the exudates as individual objects, the SVM classifier reduced the false positive rate by 29.7% and marginally increased the false negative rate by 16.2%. When evaluating all the images, we recorded a 50% reduction in the false positive rate, without any decrease in the number of false negatives. The interim results suggested that pre-scanning the samples using the SVM prior to implementing the deep-network object detector could simultaneously improve and speed up the current hard exudates detection method, especially when there is paucity of training data.
硬性渗出物是糖尿病视网膜病变患者视网膜图像的主要临床发现之一。早期发现它们对治疗潜在疾病有重大影响；因此，需要具有高可靠性的自动化系统。我们提出了一种用于识别和定位视网膜图像中硬性渗出物的新方法。为了实现快速图像预扫描，我们将支持向量机（SVM）分类器与更快的基于区域的卷积神经网络（faster R-CNN）目标探测器结合使用，以定位渗出物。快速预扫描使用从预先训练的 ResNet-50 网络提取的特征向量过滤掉无渗出物的样本。随后，使用更快的 R-CNN 探测器对剩余的样本进行详细分析。当将所有渗出物作为单个对象进行评估时，SVM 分类器将假阳性率降低了 29.7%，并略微将假阴性率提高了 16.2%。当评估所有图像时，我们记录到假阳性率降低了 50%，而假阴性率没有任何下降。中期结果表明，在使用深度网络目标探测器之前，使用 SVM 对样本进行预扫描可以同时改进和加快当前的硬性渗出物检测方法，尤其是在训练数据不足的情况下。
