Abstract
Purpose: To develop and validate a deep transfer learning (DTL) algorithm for detecting abnormalities in fundus images from non-mydriatic fundus photography examinations. Methods: A total of 1295 fundus images were collected to develop and validate a DTL algorithm for detecting abnormal fundus images. After removing 366 poor images, the DTL model was developed using 929 (370 normal and 559 abnormal) fundus images. Data preprocessing was performed to normalize the images. The inception-ResNet-v2 architecture was applied to achieve transfer learning. We tested our model using a subset of the publicly available Messidor dataset (using 366 images) and evaluated the testing performance of the DTL model for detecting abnormal fundus images. Results: In the internal validation dataset ( n = 273 images), the area under the curve (AUC), sensitivity, accuracy, and specificity of DTL for correctly classified fundus images were 0.997%, 97.41%, 97.07%, and 96.82%, respectively. For the test dataset ( n = 273 images), the AUC, sensitivity, accuracy, and specificity of the DTL for correctly classifying fundus images were 0.926%, 88.17%, 87.18%, and 86.67%, respectively. Conclusion: DTL showed high sensitivity and specificity for detecting abnormal fundus-related diseases. Further research is necessary to improve this method and evaluate the applicability of DTL in community health-care centers.
Keywords
Subject
Publication details
- DOI
- 10.4103/joco.joco_123_20
- Journal
- Journal of Current Ophthalmology, 32(4), 368-374
- Publisher
- Wolters Kluwer Medknow Publications
- Open access
- Gold open access
- License
- CC BY-NC-SA 4.0
Cite this article
APA 7
Yu, Y., Chen, X., Zhu, X., Zhang, P., Hou, Y., Zhang, R., & Wu, C. (2020). Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images. Journal of Current Ophthalmology, 32(4), 368-374. https://doi.org/10.4103/joco.joco_123_20
MLA 9
Yu, Yan, et al. "Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images." Journal of Current Ophthalmology, vol. 32, no. 4, 2020, pp. 368-374. https://doi.org/10.4103/joco.joco_123_20.
Chicago (author–date)
Yu, Yan, Xiao Chen, XiangBing Zhu, Pengfei Zhang, Yinfen Hou, Rongrong Zhang, and Changfan Wu. 2020. "Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images." Journal of Current Ophthalmology 32 (4): 368-374. https://doi.org/10.4103/joco.joco_123_20.
Harvard
Yu, Y., Chen, X., Zhu, X., Zhang, P., Hou, Y., Zhang, R. and Wu, C. (2020) 'Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images', Journal of Current Ophthalmology, 32(4), pp. 368-374. doi:10.4103/joco.joco_123_20.
Vancouver
Yu Y, Chen X, Zhu X, Zhang P, Hou Y, Zhang R, et al. Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images. Journal of Current Ophthalmology. 2020;32(4):368-374. doi:10.4103/joco.joco_123_20
IEEE
Y. Yu, X. Chen, X. Zhu, P. Zhang, Y. Hou, R. Zhang, and C. Wu, "Performance of Deep Transfer Learning for Detecting Abnormal Fundus Images," Journal of Current Ophthalmology, vol. 32, no. 4, pp. 368-374, 2020, doi: 10.4103/joco.joco_123_20.