A dataset of 10880 B-scans from 85 Uveitic patients is collected and graded by two board-certified ophthalmologists for the presence of biomarkers. A novel image classification framework, Dilated Depthwise Separable Convolution ResNet (DDSC-RN) with SVM classifier, is developed to achieve network compression with a larger receptive field that captures both low and high-level features of the biomarkers without loss of classification accuracy. The severity level of each biomarker is predicted from the feature map, extracted by the proposed DDSC-RN network.
The proposed hybrid model is evaluated using ground truth labels from the hospital. The deep learning model initially, identified the presence of biomarkers in B-scans. It achieved an overall accuracy of 98.64%, which is comparable to the performance of other state-of-the-art models, such as DRN-C-42 and ResNet-34. The SVM classifier then predicted the severity of each biomarker, achieving an overall accuracy of 89.3%.
A new hybrid model accurately identifies four retinal biomarkers on a tissue map and predicts their severity. The model outperforms other methods for identifying multiple biomarkers in complex OCT B-scans. This helps clinicians to screen multiple B-scans of UME more effectively, leading to better treatment outcomes.
■收集来自85名葡萄膜患者的10880个B扫描的数据集,并由两名董事会认证的眼科医生对生物标志物的存在进行分级。一种新颖的图像分类框架,带有SVM分类器的扩展深度可分离卷积ResNet(DDSC-RN),开发用于实现具有较大感受域的网络压缩,该感受域捕获生物标志物的低级和高级特征而不损失分类准确性。从特征图预测每个生物标志物的严重程度,通过提出的DDSC-RN网络提取。
■使用医院的地面实况标签对所提出的混合模型进行了评估。最初的深度学习模型,在B扫描中确定了生物标志物的存在。它达到了98.64%的整体精度,这与其他最先进的模型的性能相当,例如DRN-C-42和ResNet-34。然后SVM分类器预测每个生物标志物的严重程度,总体准确率为89.3%。
■一种新的混合模型在组织图上准确识别了四种视网膜生物标志物,并预测了它们的严重程度。该模型优于用于识别复杂OCTB扫描中的多种生物标志物的其他方法。这有助于临床医生更有效地筛选UME的多个B扫描,导致更好的治疗结果。