特征自适应过滤的视网膜病变分级算法

Feature-adaptive filtering for retinopathy grading

针对视网膜病变图像特征识别困难以及病变分级效率不高等问题,提出一种特征自适应过滤的视网膜病变分级算法。首先,算法利用ResNet-50网络构建的多尺度过滤分支(MFB)对视网膜病变图像进行逐级特征提取;其次,在不同尺度的过滤分支后级联自适应特征过滤块(AFFB)对视网膜病变图像进行特征增强与过滤;然后,使用特征互补融合模块(FCFM)对特征过滤后的多个局部增强特征进行信息互补,并通过聚合局部增强特征的互补信息丰富视网膜病变图像的局部细节;最后,采用细粒度分类损失与焦点损失对具有不同局部特征信息的分级模型进行训练,并在IDRiD数据集上进行实验。实验结果表明,所提分级算法的准确率为80.58%、加权Kappa系数为88.70%、特异性为94.20%、敏感性为94.10%,该算法能有效识别视网膜病变图像的细微病变区域并提高分级效率。

To address the difficulty in recognizing features of retinopathy images and the low efficiency of disease grading,a retinopathy grading algorithm based on feature adaptive filtering was proposed.Firstly,the algorithm used the multi-scale filtering branches(MFB)constructed by the ResNet-50 network to extract features of retinopathy images step by step.Secondly,cascade adaptive feature filter blocks(AFFB)was adopted after filtering branches of different scales to perform feature enhancement and filtering on retinopathy images.Then,the feature complementary fusion module(FCFM)was utilized to complement the multiple local enhancement features after feature filtering,and enrich the local details of the retinopathy image by aggregating the complementary information of the local enhancement features.Hierarchical models with different local feature information were trained and experiments were performed on the IDRiD dataset.The experimental results show that the accuracy of the proposed grading algorithm was 80.58%,the weighted kappa coefficient 88.70%,the specificity 94.20%,and the sensitivity 94.10%.The algorithm could effectively identify the subtle lesions in retinopathy images and improve the grading efficiency.