Lesion Detection and Grading of Diabetic Retinopathy via Two-Stages Deep Convolutional Neural Networks
Abstract
We propose an automatic diabetic retinopathy (DR) analysis algorithm based on two-stages deep convolutional neural networks (DCNN). Compared to existing DCNN-based DR detection methods, the proposed algorithm has the following advantages: (1) Our algorithm can not only point out the lesions in fundus color images, but also give the severity grades of DR. (2) By introducing an imbalanced weighting scheme, more attentions will be payed on lesion patches for DR grading, which significantly improves the performance of DR grading under the same implementation setup. In this study, we label 12, 206 lesion patches and re-annotate the DR grades of 23, 595 fundus images from Kaggle competition dataset. Under the guidance of clinical ophthalmologists, the experimental results show that our lesion detection net achieves comparable performance with trained human observers, and the proposed imbalanced weighted scheme also be proved to significantly enhance the capability of our DCNN-based DR grading algorithm.
Keywords
Diabetic retinopathy Deep convolutional neural networks Fundus images Retinopathy lesionsReferences
- 1.Shaw, J.E., Sicree, R.A., Zimmet, P.Z.: Global estimates of the prevalence of diabetes for 2010 and 2030. Diabetes Res. Clin. Pract. 87(1), 4–14 (2010)CrossRefGoogle Scholar
- 2.Pratt, H., Coenen, F., Broadbent, D.M.: Convolutional neural networks for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)CrossRefGoogle Scholar
- 3.Bhaskaranand, M., Cuadros, J., Ramachandra, C., et al.: EyeArt + EyePACS: automated retinal image analysis for diabetic retinopathy screening in a telemedicine system. In: OMIA (2015)Google Scholar
- 4.Haloi, M.: Improved microaneurysm detection using deep neural networks. arXiv preprint (2015). arXiv:1505.04424v2
- 5.Gulshan, V., Peng, L., Coram, M., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316(22), 2402–2410 (2016)CrossRefGoogle Scholar
- 6.Kocur, I., Resnikoff, S.: Visual impairment and blindness in Europe and their prevention. Br. J. Ophthalmol. 86(7), 716C722 (2002)CrossRefGoogle Scholar
- 7.van Grinsven, M.J., van Ginneken, B., Hoyng, C.B., et al.: Fast convolutional neural network training using selective data sampling: application to hemorrhage detection in color fundus images. IEEE Trans. Med. Imaging 35(5), 1273–1284 (2016)CrossRefGoogle Scholar
- 8.Seoud, L., Chelbi, J., Cheriet, F.: Automatic grading of diabetic retinopathy on a public database. In: OMIA (2015)Google Scholar
- 9.American Academy of Ophthalmology. International Clinical Diabetic Retinopathy Disease Severity Scale (2012). http://www.icoph.org/dynamic/attachments/resources/diabetic-retinopathy-detail.pdf
- 10.Haloi, M., Dandapat, S., Sinha, R.: A Gaussian scale space approach for exudates detection classification and severity prediction. arXiv preprint (2015). arXiv:1505.00737
- 11.Srivastava, R., Duan, L., Wong, D.W.K., et al.: Detecting retinal microaneurysms and hemorrhages with robustness to the presence of blood vessels. Comput. Methods Programs Biomed. 138, 83–91 (2017)CrossRefGoogle Scholar
- 12.Sankar, M., Batri, K., Parvathi, R.: Earliest diabetic retinopathy classification using deep convolution neural networks. Int. J. Adv. Eng. Technol. 7, 466–470 (2016)Google Scholar
- 13.Gu, J., Wang, Z., Kuen, J., et al.: Recent advances in convolutional neural networks. arXiv preprint (2016). arXiv:1512.07108v2
- 14.Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: NIPS (2012)Google Scholar
- 15.Szegedy, C., Liu, W., Jia, Y., et al.: Going deeper with convolutions. In: CVPR (2015)Google Scholar
- 16.He, K., Zhang, X., Ren, S., et al.: Deep residual learning for image recognition. In: CVPR (2016)Google Scholar
- 17.Bishop, C.M.: Pattern Recognition and Machine Learning. Information Science and Statistics. Springer, New York (2006)zbMATHGoogle Scholar
- 18.Cortes, C., Vapnik, V.: Support-vector networks. Mach. Learn. 20(3), 273–297 (1995)zbMATHGoogle Scholar
- 19.Kam, H.T.: The random subspace method for constructing decision forests. IEEE Trans. Pattern Anal. Mach. Intell. 20(8), 832–844 (1998)CrossRefGoogle Scholar