Beijing Tongren Eye Center, Beijing Institute of Ophthalmology, Beijing Tongren Hospital, Capital Medical University, Beijing Key Laboratory of Ophthalmology and visual Sciences, National Engineering Research Center for Ophthalmology, Beijing, China.
Key Laboratory of Universal Wireless Communations, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China.
Comput Methods Programs Biomed. 2019 Dec;182:104978. doi: 10.1016/j.cmpb.2019.07.006. Epub 2019 Aug 5.
The shortage of ophthalmologists in rural areas in China causes a lot of cataract patients not getting timely diagnosis and effective treatment. We develop an algorithm and platform to automatically diagnose and grade cataract based on fundus images of patients. This method can help government assisting poor population more accurately.
The novel six-level cataract grading method proposed in this paper focuses on the multi-feature fusion based on stacking. We extract two kinds of features which can effectively distinguish different levels of cataract. One is high-level features extracted from residual network (ResNet18). The other is texture features extarcted by gray level co-occurrence matrix (GLCM). Then a frame is proposed to automatically grade cataract by the extracted features. In the frame, two support vector machine (SVM) classifiers are used as base-learners to obtain the probability outputs of each fundus image, and fully connected neural network (FCNN) are used as meta-learner to output the final classification result, which consists of two fully-connected layers.
The accuracy of six-level grading achieved by the proposed method is up to 92.66% on average, the highest of which reaches 93.33%. The proposed method achieves 94.75% accuracy on four-level grading for cataract, which is at least 1.75% higher than those of the exiting methods.
Six-category cataract classification algorithm show that Multi-feature & Stacking proposed in this paper helps achieve higher grading performance and lower volatility than grading using high-level features and texture features respectively. We also apply our algorithm into four-level cataract grading system and it shows higher accuracy compared with previous reports.
中国农村地区眼科医生短缺，导致大量白内障患者无法得到及时诊断和有效治疗。我们开发了一种基于患者眼底图像自动诊断和分级白内障的算法和平台。该方法可以帮助政府更准确地帮助贫困人口。
本文提出的六级白内障分级方法侧重于基于堆叠的多特征融合。我们提取了两种能够有效区分不同程度白内障的特征。一种是从残差网络（ResNet18）中提取的高级特征。另一种是通过灰度共生矩阵（GLCM）提取的纹理特征。然后提出了一个框架，通过提取的特征自动对白内障进行分级。在该框架中，两个支持向量机（SVM）分类器作为基学习器，获得每个眼底图像的概率输出，全连接神经网络（FCNN）作为元学习器，输出最终的分类结果，该结果由两个全连接层组成。
该方法在六级分级中的准确率平均达到 92.66%，最高可达 93.33%。该方法在四级白内障分级中的准确率达到 94.75%，比现有方法至少高出 1.75%。
六级白内障分类算法表明，本文提出的多特征与堆叠方法有助于实现比单独使用高级特征和纹理特征更高的分级性能和更低的波动性。我们还将我们的算法应用于四级白内障分级系统，与之前的报告相比，它显示出更高的准确性。
