Auto-Classification of Retinal Diseases in the Limit of Sparse Data Using a Two-Streams Machine Learning Model
This work addresses the challenge of limited access to specialists in ophthalmology by enabling automated diagnosis, though it appears incremental as it builds on existing methods like SVM and DNNs.
The authors tackled the problem of automatic clinical diagnosis of retinal diseases in data-scarce settings by proposing a two-streams hybrid model combining SVM and DNNs, achieving 90.43% accuracy on a new dataset with 52 disease classes, which is comparable to professional ophthalmologists.
Automatic clinical diagnosis of retinal diseases has emerged as a promising approach to facilitate discovery in areas with limited access to specialists. Based on the fact that fundus structure and vascular disorders are the main characteristics of retinal diseases, we propose a novel visual-assisted diagnosis hybrid model mixing the support vector machine (SVM) and deep neural networks (DNNs). Furthermore, we present a new clinical retina dataset, called EyeNet2, for ophthalmology incorporating 52 retina diseases classes. Using EyeNet2, our model achieves 90.43\% diagnosis accuracy, and the model performance is comparable to the professional ophthalmologists.