Application of deep-transfer learning in automatic glaucoma detection

The existing glaucoma diagnosis methods heavily rely on the scale of training datasets. To solve this problem, in this paper, we propose a deep-transfer learning network to automatically diagnose glaucoma. This network can better capture the discriminant glaucoma-related features under limited supervision. Particularly, we adopt a convolutional neural network (CNN) to transfer the general features from similar ophthalmic datasets and then use the maximum mean discrepancy to reduce the feature gap and refine the specific features. To verify the effectiveness of the proposed method, we conducted experiments on real-world datasets. Compared with other models, our method achieved better classification performance, with an accuracy, sensitivity, and specificity of 91. 15%, 90. 13%, and 92. 25%, respectively, having certain medical values and importance for the early screening of glaucoma. © 2023 Editorial Board of Journal of Harbin Engineering. All rights reserved.