Hongyan Zhang1, Kai Niu2, Yanmin Xiong2, Weihua Yang3, ZhiQiang He4, Hongxin Song5. 1. 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. 2. Key Laboratory of Universal Wireless Communations, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China. 3. The First People's Hospital of Huzhou, Huzhou, Zhejiang, China. 4. Key Laboratory of Universal Wireless Communations, Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China; College of Big Data and Information Engineering, Guizhou University, Guizhou, China. Electronic address: hezq@bupt.edu.cn. 5. 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. Electronic address: songhongxin2012@163.com.
Abstract
BACKGROUND AND OBJECTIVE: 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. METHODS: 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. RESULT: 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. CONCLUSIONS: 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.
BACKGROUND AND OBJECTIVE: The shortage of ophthalmologists in rural areas in China causes a lot of cataractpatients 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. METHODS: 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. RESULT: 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. CONCLUSIONS: 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.
Authors: Laura Gutierrez; Jane Sujuan Lim; Li Lian Foo; Wei Yan Ng; Michelle Yip; Gilbert Yong San Lim; Melissa Hsing Yi Wong; Allan Fong; Mohamad Rosman; Jodhbir Singth Mehta; Haotian Lin; Darren Shu Jeng Ting; Daniel Shu Wei Ting Journal: Eye Vis (Lond) Date: 2022-01-07
Authors: Xing Wu; Di Xu; Tong Ma; Zhao Hui Li; Zi Ye; Fei Wang; Xiang Yang Gao; Bin Wang; Yu Zhong Chen; Zhao Hui Wang; Ji Li Chen; Yun Tao Hu; Zong Yuan Ge; Da Jiang Wang; Qiang Zeng Journal: Front Cell Dev Biol Date: 2022-07-22